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REVIEW 3 major objections 5 minor 193 references

Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This review argues that embedding physics into neural networks currently gives the strongest overall performance in crowd behaviour prediction, combining accuracy with explainability.

desk verdict Useful review, but the claim that physics-inspired deep learning is most accurate rests on a mixed-protocol table and mostly the authors' own results. read the letter →

arxiv 2505.18401 v1 pith:GTPPZ7RC submitted 2025-05-23 cs.CV

Jiangbei Yue , He Wang This is my paper · ORCID
classification cs.CV MSC 68T4568T07
keywords crowdbehaviouranalysistrajectorypredictionrecognitionphysics-inspireddeeplearningsocialforcemodelneuraldifferentialequationsreviewADE/FDEmetrics
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Deep learning has changed how researchers predict and recognise crowd behaviour, and this review chapter maps that landscape by classifying methods according to their network architecture: RNNs, CNNs, GNNs, generative models, transformers, and a newer family that embeds physics into neural networks. Its central conclusion is that the physics-inspired family currently performs best overall in trajectory prediction, delivering the lowest or near-lowest errors on standard benchmarks while also providing something most deep models lack: human-understandable explanations. The authors reach this by combining a qualitative comparison of accuracy, explainability, data requirements, and computational cost with a quantitative table of Average and Final Displacement Error on the ETH/UCY and SDD datasets. If the conclusion holds, it gives safety-critical applications such as autonomous driving and crowd management a concrete reason to favour hybrid physics-neural models over black-box networks.

What carries the argument

The argument is carried by a two-part comparative structure: a taxonomy that sorts methods by network architecture, and a numerical table that compares representative models on the ETH/UCY and SDD benchmarks using Average Displacement Error and Final Displacement Error. Within that structure, the physics-inspired family's defining mechanism is a neural network whose dynamics are constrained by an explicit physical system: the social force model of Helbing and Molnar embedded as a neural differential equation in NSP-SFM, the material point method with active-matter stress and Toner-Tu active forces in CrowdMPM, and an inverted-pendulum model with learned balance-recovery and interaction forces in LDP. This explicit physical backbone is what the review points to for both the accuracy gains and the explainability advantage.

What would settle it

Re-run the representative methods from Table 2 under one unified evaluation protocol, with the same training and validation splits, the same number of sampled trajectories, and the same random seeds, and check whether NSP-SFM and NDCPM still hold the top ADE/FDE positions. A simpler version: re-evaluate NSP-SFM using the deterministic protocol applied to methods marked with an asterisk, and re-evaluate a deterministic baseline using the min-over-20 protocol; if the ordering between the physics-inspired and pure deep learning families changes, the central claim fails.

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Extended reading notes

Core claim

The chapter's central claim, stated in the prediction section's conclusion, is that physics-inspired deep learning methods demonstrate the strongest overall performance, particularly in accuracy and explainability. On the quantitative side, two such methods, NSP-SFM and NDCPM, occupy the top of the comparison table, with NSP-SFM reporting an ADE/FDE of 0.17/0.24 on ETH/UCY and 6.52/10.61 on SDD, and NDCPM reporting 0.15/0.33 on ETH/UCY. On the qualitative side, the review rates physics-inspired methods as very high in accuracy and high in explainability, whereas pure deep learning families are rated very high in accuracy but low in explainability. The review also credits the physics component with reducing data requirements, because the physical model supplies structure the network would otherwise have to learn from data.

Load-bearing premise

The ranking assumes that the error scores reported by different papers can be compared directly, even though stochastic methods report the best of 20 random guesses while deterministic methods report a single prediction; if that difference changes the scores, the conclusion that physics-inspired methods are most accurate is unsupported.

Editorial extensions

If this is right

  • If the chapter's conclusion is correct, research effort in trajectory prediction should shift toward physics-inspired architectures, since they are the family that combines top accuracy with explainability.
  • Physical priors reduce the amount of training data needed, making physics-inspired models a better fit for deployment settings where large trajectory datasets do not exist, such as unusual events or new environments.
  • The physics-inspired principle is portable: continuum and active-matter versions already address dense crowds, and full-body versions address physical perturbations, so the approach is not limited to sparse 2D trajectories.
  • In safety-critical applications, predictions can be audited: instead of a black-box output, a planner or human operator can inspect the goal-attraction, collision-repulsion, and environment forces that produced a forecast.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A fair benchmark that standardises the stochastic-versus-deterministic evaluation protocol could reorder the accuracy table; the paper's own footnote leaves this open, so the accuracy lead should be read as provisional until such a benchmark exists.
  • The physics-inspired advantage may transfer to crowd behaviour recognition, where physics-based features such as entropy, order parameters, and active-Langevin group detection already appear; testing whether physics-informed inductive biases improve recognition accuracy would be a natural extension.
  • Combining the two dense-crowd directions the review highlights, continuum active-matter models and full-body latent differentiable physics, could yield video-driven risk prediction for crowd crushes, a use case the authors say is currently bottlenecked by data.
  • Because the review rates physics-inspired methods as needing only medium data, a concrete test is to measure how each architecture family's accuracy degrades as training data shrinks; the physics component should flatten the degradation curve if the claim is right.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper is a review chapter on recent deep-learning research in crowd behaviour analysis, organized around two core tasks: crowd behaviour prediction and crowd behaviour recognition. For prediction, it surveys traditional statistical machine learning, deep network families (RNN, CNN, GNN, generative, and transformer), and physics-inspired deep learning methods, with detailed case studies of Social-LSTM, NSP-SFM, CrowdMPM, and LDP. For recognition, it covers holistic and individual-based traditional methods as well as CNN, RNN, GNN, and transformer approaches. The chapter concludes that physics-inspired deep learning methods show the strongest overall performance, particularly in accuracy and explainability.

Significance. The review is likely useful as a structured introduction to the field: it covers a broad and current literature, gives explicit equations for representative methods, and offers a consistent taxonomy across both tasks. If the comparative conclusion were properly supported, it would help direct future research toward physics-based priors. However, the central quantitative claim is currently undermined by the protocol mixing in Table 2 and by the non-uniform numbers reported there, so the significance of the conclusion as stated is limited. The descriptive portions and the discussion of future directions (data infrastructure, unsupervised learning, high-density crowds) are the strongest parts of the manuscript.

major comments (3)
  1. [Table 2; §2.4] The footnote to Table 2 states that stochastic methods report the minimum ADE/FDE over 20 sampled trajectories, whereas asterisked deterministic methods report a single deterministic prediction. These quantities are not commensurable: the min over 20 samples improves as more samples are drawn and reflects the best-case prediction, not expected performance. Section 2.4 first notes that "evaluation metrics and experimental settings vary greatly across different publications" and then asserts that a numerical comparison is possible because most deep learning methods "share common datesets and evaluation metrics"; this is internally contradictory. Since the conclusion that "physics-inspired deep learning methods demonstrate the strongest overall performance, particularly in accuracy" rests on Table 2, the ranking must be recomputed under a matched protocol (e.g., best-of-20 for every method, or mean over samples), or the accuracy claim must be downgraded to a qualitative statement.
  2. [Table 2] Even accepting the reported values, Table 2 does not establish that physics-inspired methods are uniformly most accurate. NSP-SFM's ETH/UCY ADE (0.17) is worse than NDCPM's (0.15), and its SDD ADE (6.52) is worse than IDM's (6.38). Thus the assertion that physics-inspired deep learning methods "achieve the highest accuracy" requires a weighting of ADE against FDE and against dataset-specific performance that is not specified in the text. The conclusion should either name the precise criterion under which NSP-SFM is best (for example, the lowest FDE on both benchmarks) or be softened to "competitive accuracy with additional explainability benefits."
  3. [Table 1; §2.4] The "explainability" part of the claim "strongest overall performance, particularly in accuracy and explainability" is not supported by any quantitative or even semi-quantitative evidence in Table 1; the table is a high-level qualitative comparison. The manuscript should define explainability operationally in this context (e.g., the presence of physically interpretable parameters such as forces, masses, and interaction potentials) and should clearly state that the explainability comparison is a judgment, not a measured outcome.
minor comments (5)
  1. [§2.3.1] The acronym "NSF-SFM" appears in the sentence "Also, since the learn SFM is essentially a simulator, NSF-SFM can simulate more pedestrian behaviours..."; this should be "NSP-SFM" and "learned SFM."
  2. [§2.4 and Table 2 caption] There are repeated typos: "datesets" and "common datesets" should be "datasets" and "common datasets."
  3. [§4.1 and References] The citation "[Velayutham et al.]" lacks a year and the corresponding reference entry is incomplete; it should be completed with the full bibliographic details.
  4. [§2.4] The sentence "employing deep learning techniques such as RNNs, CNNs, and GNNs has substantially improved prediction accuracy, reducing ADE and FDE by approximately 20% and 30%, respectively, on ETH/UCY, and by about 50% for both metrics on SDD" reports summary percentages without showing their computation; either cite the source or show the derivation.
  5. [§2.3.2] There is a typographical error in "CrowdMPM He et al. [2025] is the first method of its kind,," with a double comma.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the review's comparative conclusions are assembled from independently reported benchmark results, and the authorial self-citations are not load-bearing.

full rationale

The chapter is a literature review, not a derivation, and its central claim about physics-inspired deep learning does not reduce to its own inputs. The Section 2.4 conclusion that 'physics-inspired deep learning methods demonstrate the strongest overall performance, particularly in accuracy and explainability' rests on a qualitative synthesis and on Table 2, which reports ADE/FDE values taken from the cited publications. The highlighted NSP-SFM row is an authorial work, but the numbers are externally evaluated benchmark results, and the table also includes independent physics-inspired methods such as NDCPM. No equation in the paper constructs the conclusion from a definition, no fitted parameter is renamed as a prediction, and no uniqueness theorem or ansatz is imported through self-citation. The mixed evaluation protocol disclosed in the Table 2 footnote—minimum error over 20 sampled trajectories for stochastic methods versus a single trajectory for deterministic ones—is a real methodological comparability concern, but it is a correctness/validity issue rather than circularity. The paper explicitly acknowledges in Section 2.4 that 'the evaluation metrics and experimental settings vary greatly across different publications,' so the limitation is not concealed. Self-citations to NSP, CrowdMPM, and LDP are used for detailed exposition and as representative entries, but the review's coverage and comparative basis include many external works, and the central conclusion is not forced by a self-citation chain. No specific circular step meeting the quoted-reduction standard was found.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The review introduces no new parameters, models, or entities. Its conclusions rest on the accuracy of the cited literature's reported results and on the comparability of heterogeneous evaluation protocols.

assumptions (3)
  • domain assumption Reported ADE/FDE numbers in Table 2 are accurately transcribed from the cited papers.
    The review's quantitative comparison and conclusion depend on these numbers being correct.
  • domain assumption Methods classified as stochastic in Table 2 all use the same evaluation protocol (20 samples, minimum error), making cross-method comparison within that group valid.
    The table footnote states this protocol for stochastic methods; the conclusion depends on comparability.
  • domain assumption The selected representative methods are representative of their categories.
    The review selects 'some representative papers' and generalizes to the whole category.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review." pith.science (2026). https://pith.science/paper/GTPPZ7RC

@misc{pith2026250518401,
  author       = {Pith},
  title        = {Pith review of: Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GTPPZ7RC}},
  note         = {Machine review of arXiv:2505.18401}
}
read the original abstract

Crowd behaviour analysis is essential to numerous real-world applications, such as public safety and urban planning, and therefore has been studied for decades. In the last decade or so, the development of deep learning has significantly propelled the research on crowd behaviours. This chapter reviews recent advances in crowd behaviour analysis using deep learning. We mainly review the research in two core tasks in this field, crowd behaviour prediction and recognition. We broadly cover how different deep neural networks, after first being proposed in machine learning, are applied to analysing crowd behaviours. This includes pure deep neural network models as well as recent development of methodologies combining physics with deep learning. In addition, representative studies are discussed and compared in detail. Finally, we discuss the effectiveness of existing methods and future research directions in this rapidly evolving field. This chapter aims to provide a high-level summary of the ongoing deep learning research in crowd behaviour analysis. It intends to help new researchers who just entered this field to obtain an overall understanding of the ongoing research, as well as to provide a retrospective analysis for existing researchers to identify possible future directions

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Works this paper leans on

193 extracted references · 70 canonical work pages

  1. [1]

    Social lstm: Human trajectory prediction in crowded spaces

    Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, and Silvio Savarese. Social lstm: Human trajectory prediction in crowded spaces. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 961--971, 2016

    2016

  2. [2]

    Social ways: Learning multi-modal distributions of pedestrian trajectories with gans

    Javad Amirian, Jean-Bernard Hayet, and Julien Pettr \'e . Social ways: Learning multi-modal distributions of pedestrian trajectories with gans. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0--0, 2019

    2019

  3. [3]

    Context-aware trajectory prediction

    Federico Bartoli, Giuseppe Lisanti, Lamberto Ballan, and Alberto Del Bimbo. Context-aware trajectory prediction. In 2018 24th International Conference on Pattern Recognition (ICPR), pages 1941--1946. IEEE, 2018

    2018

  4. [4]

    Crowd characterization in surveillance videos using deep-graph convolutional neural network

    Shreetam Behera, Debi Prosad Dogra, Malay Kumar Bandyopadhyay, and Partha Pratim Roy. Crowd characterization in surveillance videos using deep-graph convolutional neural network. IEEE Transactions on Cybernetics, 53 0 (6): 0 3428--3439, 2021 a

    2021

  5. [5]

    Understanding crowd flow patterns using active-langevin model

    Shreetam Behera, Debi Prosad Dogra, Malay Kumar Bandyopadhyay, and Partha Pratim Roy. Understanding crowd flow patterns using active-langevin model. Pattern Recognition, 119: 0 108037, 2021 b

    2021

  6. [6]

    Characterization of orderly behavior of human crowd in videos using deep learning

    Shreetam Behera, Shaily Preetham Kurra, and Debi Prosad Dogra. Characterization of orderly behavior of human crowd in videos using deep learning. In Intelligence Science III: 4th IFIP TC 12 International Conference, ICIS 2020, Durgapur, India, February 24--27, 2021, Revised Selected Papers 4, pages 217--226. Springer, 2021 c

    2020

  7. [7]

    Pidlnet: A physics-induced deep learning network for characterization of crowd videos

    Shreetam Behera, Thakare Kamalakar Vijay, H Manish Kausik, and Debi Prosad Dogra. Pidlnet: A physics-induced deep learning network for characterization of crowd videos. In 2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pages 1--8. IEEE, 2021 d

    2021

  8. [8]

    Ensemble classification of video-recorded crowd movements

    Mounir Bendali-Braham, Jonathan Weber, Germain Forestier, Lhassane Idoumghar, and Pierre-Alain Muller. Ensemble classification of video-recorded crowd movements. In 2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA), pages 152--158. IEEE, 2021

    2021

Show all 193 references
  1. [9]

    Group lstm: Group trajectory prediction in crowded scenarios

    Niccol \'o Bisagno, Bo Zhang, and Nicola Conci. Group lstm: Group trajectory prediction in crowded scenarios. In Proceedings of the European conference on computer vision (ECCV) workshops, pages 213--225, 2018

    2018

  2. [10]

    Embedding group and obstacle information in lstm networks for human trajectory prediction in crowded scenes

    Niccol \'o Bisagno, Cristiano Saltori, Bo Zhang, Francesco GB De Natale, and Nicola Conci. Embedding group and obstacle information in lstm networks for human trajectory prediction in crowded scenes. Computer Vision and Image Understanding, 203: 0 103126, 2021

    2021

  3. [11]

    An introduction to the kalman filter

    Gary Bishop, Greg Welch, et al. An introduction to the kalman filter. Proc of SIGGRAPH, Course, 8 0 (27599-23175): 0 41, 2001

    2001

  4. [12]

    Latent dirichlet allocation

    David M Blei, Andrew Y Ng, and Michael I Jordan. Latent dirichlet allocation. Journal of machine Learning research, 3 0 (Jan): 0 993--1022, 2003

    2003

  5. [13]

    A short review of deep learning methods for understanding group and crowd activities

    Luis Felipe Borja-Borja, Marcelo Saval-Calvo, and Jorge Azorin-Lopez. A short review of deep learning methods for understanding group and crowd activities. In 2018 International Joint Conference on Neural Networks (IJCNN), pages 1--8. IEEE, 2018

    2018

  6. [14]

    Pedestrian models for autonomous driving part ii: high-level models of human behavior

    Fanta Camara, Nicola Bellotto, Serhan Cosar, Florian Weber, Dimitris Nathanael, Matthias Althoff, Jingyuan Wu, Johannes Ruenz, Andr \'e Dietrich, Gustav Markkula, et al. Pedestrian models for autonomous driving part ii: high-level models of human behavior. IEEE Transactions on...

    2020

  7. [15]

    Quo vadis, action recognition? a new model and the kinetics dataset

    Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6299--6308, 2017

    2017

  8. [16]

    Fight detection with spatial and channel wise attention-based convlstm model

    Kunal Chaturvedi, Chhavi Dhiman, and Dinesh Kumar Vishwakarma. Fight detection with spatial and channel wise attention-based convlstm model. Expert systems, 41 0 (1): 0 e13474, 2024

    2024

  9. [17]

    Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs

    Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40 0 (4): 0 ...

    2017

  10. [18]

    Neural ordinary differential equations

    Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud. Neural ordinary differential equations. Advances in neural information processing systems, 31, 2018

    2018

  11. [19]

    Multimodal pedestrian trajectory prediction using probabilistic proposal network

    Weihuang Chen, Zhigang Yang, Lingyang Xue, Jinghai Duan, Hongbin Sun, and Nanning Zheng. Multimodal pedestrian trajectory prediction using probabilistic proposal network. IEEE Transactions on Circuits and Systems for Video Technology, 33 0 (6): 0 2877--2891, 2022

    2022

  12. [20]

    Infogan: Interpretable representation learning by information maximizing generative adversarial nets

    Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. Infogan: Interpretable representation learning by information maximizing generative adversarial nets. Advances in neural information processing systems, 29, 2016

    2016

  13. [21]

    Three-dimensional atrous inception module for crowd behavior classification

    Jong-Hyeok Choi, Jeong-Hun Kim, Aziz Nasridinov, and Yoo-Sung Kim. Three-dimensional atrous inception module for crowd behavior classification. Scientific Reports, 14 0 (1): 0 14390, 2024

    2024

  14. [22]

    A unified framework for multi-target tracking and collective activity recognition

    Wongun Choi and Silvio Savarese. A unified framework for multi-target tracking and collective activity recognition. In Computer Vision--ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part IV 12, pages 215--230. Springer, 2012

    2012

  15. [23]

    What are they doing?: Collective activity classification using spatio-temporal relationship among people

    Wongun Choi, Khuram Shahid, and Silvio Savarese. What are they doing?: Collective activity classification using spatio-temporal relationship among people. In 2009 IEEE 12th international conference on computer vision workshops, ICCV Workshops, pages 1282--1289. IEEE, 2009

    2009

  16. [24]

    Empirical evaluation of gated recurrent neural networks on sequence modeling

    Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. In NIPS 2014 Workshop on Deep Learning, December 2014, 2014

    2014

  17. [25]

    A recurrent latent variable model for sequential data

    Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C Courville, and Yoshua Bengio. A recurrent latent variable model for sequential data. Advances in neural information processing systems, 28, 2015

    2015

  18. [26]

    Social-vrnn: One-shot multi-modal trajectory prediction for interacting pedestrians

    Bruno Ferreira de Brito, Hai Zhu, Wei Pan, and Javier Alonso-Mora. Social-vrnn: One-shot multi-modal trajectory prediction for interacting pedestrians. In Conference on Robot Learning, pages 862--872. PMLR, 2021

    2021

  19. [27]

    Behavior recognition based on category subspace in crowded videos

    Chunhua Deng, Xiaoge Kang, Ziqi Zhu, and Shiqian Wu. Behavior recognition based on category subspace in crowded videos. IEEE Access, 8: 0 222599--222610, 2020

    2020

  20. [28]

    Bert: Pre-training of deep bidirectional transformers for language understanding

    Jacob Devlin. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018

    2018 arXiv

  21. [29]

    Modelling pedestrian trajectory patterns with gaussian processes

    David Ellis, Eric Sommerlade, and Ian Reid. Modelling pedestrian trajectory patterns with gaussian processes. In 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pages 1229--1234. IEEE, 2009

    2009

  22. [30]

    The ensemble kalman filter: Theoretical formulation and practical implementation

    Geir Evensen. The ensemble kalman filter: Theoretical formulation and practical implementation. Ocean dynamics, 53: 0 343--367, 2003

    2003

  23. [31]

    Citymomentum: an online approach for crowd behavior prediction at a citywide level

    Zipei Fan, Xuan Song, Ryosuke Shibasaki, and Ryutaro Adachi. Citymomentum: an online approach for crowd behavior prediction at a citywide level. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pages 559--569, 2015

    2015

  24. [32]

    Fundamentals of neural networks: architectures, algorithms, and applications, 1994

    Laurene Fausett. Fundamentals of neural networks: architectures, algorithms, and applications, 1994

    1994

  25. [33]

    Forward propagation of a push through a row of people

    Sina Feldmann and Juliane Adrian. Forward propagation of a push through a row of people. Safety science, 164: 0 106173, 2023

    2023

  26. [34]

    Soft+ hardwired attention: An lstm framework for human trajectory prediction and abnormal event detection

    Tharindu Fernando, Simon Denman, Sridha Sridharan, and Clinton Fookes. Soft+ hardwired attention: An lstm framework for human trajectory prediction and abnormal event detection. Neural networks, 108: 0 466--478, 2018

    2018

  27. [35]

    Markov models and hidden M arkov models: A brief tutorial

    Eric Fosler-Lussier. Markov models and hidden M arkov models: A brief tutorial. International Computer Science Institute, 1998

    1998

  28. [36]

    Spatio-temporal attention transformer model for future trajectory forecast

    Zhiqiang Geng, Te Zhang, and Yongming Han. Spatio-temporal attention transformer model for future trajectory forecast. In 2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS), pages 1068--1073. IEEE, 2022

    2022

  29. [37]

    Transformer networks for trajectory forecasting

    Francesco Giuliari, Irtiza Hasan, Marco Cristani, and Fabio Galasso. Transformer networks for trajectory forecasting. In 2020 25th international conference on pattern recognition (ICPR), pages 10335--10342. IEEE, 2021

    2020

  30. [38]

    Continuum modeling of crowd turbulence

    Abhinav Golas, Rahul Narain, and Ming C Lin. Continuum modeling of crowd turbulence. Physical review E, 90 0 (4): 0 042816, 2014

    2014

  31. [39]

    Resolving collisions in dense 3d crowd animations

    Gonzalo Gomez-Nogales, Melania Prieto-Martin, Cristian Romero, Marc Comino-Trinidad, Pablo Ramon-Prieto, Anne-H \'e l \`e ne Olivier, Ludovic Hoyet, Miguel Otaduy, Julien Pettre, and Dan Casas. Resolving collisions in dense 3d crowd animations. ACM Transactions on Graphics, 43...

    2024

  32. [40]

    Generative adversarial nets

    Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in neural information processing systems, 27, 2014

    2014

  33. [41]

    Stochastic trajectory prediction via motion indeterminacy diffusion

    Tianpei Gu, Guangyi Chen, Junlong Li, Chunze Lin, Yongming Rao, Jie Zhou, and Jiwen Lu. Stochastic trajectory prediction via motion indeterminacy diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17113--17122, 2022

    2022

  34. [42]

    Social gan: Socially acceptable trajectories with generative adversarial networks

    Agrim Gupta, Justin Johnson, Li Fei-Fei, Silvio Savarese, and Alexandre Alahi. Social gan: Socially acceptable trajectories with generative adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2255--2264, 2018

    2018

  35. [43]

    Guy, Jur van den Berg, Wenxi Liu, Rynson Lau, Ming C

    Stephen J. Guy, Jur van den Berg, Wenxi Liu, Rynson Lau, Ming C. Lin, and Dinesh Manocha. A statistical similarity measure for aggregate crowd dynamics. ACM Trans. Graph., 31 0 (6), November 2012. ISSN 0730-0301. doi:10.1145/2366145.2366209. URL https://doi.org/10.1145/2366145.2366209

    2012

  36. [44]

    A survey on visual transformer

    Kai Han, Yunhe Wang, Hanting Chen, Xinghao Chen, Jianyuan Guo, Zhenhua Liu, Yehui Tang, An Xiao, Chunjing Xu, Yixing Xu, et al. A survey on visual transformer. arXiv preprint arXiv:2012.12556, 2020

    2012 arXiv

  37. [45]

    Learning spatio-temporal features with 3d residual networks for action recognition

    Kensho Hara, Hirokatsu Kataoka, and Yutaka Satoh. Learning spatio-temporal features with 3d residual networks for action recognition. In Proceedings of the IEEE international conference on computer vision workshops, pages 3154--3160, 2017

    2017

  38. [46]

    Informative scene decomposition for crowd analysis, comparison and simulation guidance

    Feixiang He, Yuanhang Xiang, Xi Zhao, and He Wang. Informative scene decomposition for crowd analysis, comparison and simulation guidance. ACM Transactions on Graphics (TOG), 2020

    2020

  39. [47]

    Learning extremely high density crowds as active matters

    Feixiang He, Jiangbei Yue, Jialin Zhu, Armin Seyfried, Dan Casas, Julien Pettr \'e , and He Wang. Learning extremely high density crowds as active matters. arXiv preprint arXiv:2503.12168, 2025

    2025 arXiv

  40. [48]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770--778, 2016

    2016

  41. [49]

    Social force model for pedestrian dynamics

    Dirk Helbing and Peter Molnar. Social force model for pedestrian dynamics. Physical review E, 51 0 (5): 0 4282, 1995

    1995

  42. [50]

    Long short-term memory

    S Hochreiter. Long short-term memory. Neural Computation MIT-Press, 1997

    1997

  43. [51]

    Approximation capabilities of multilayer feedforward networks

    Kurt Hornik. Approximation capabilities of multilayer feedforward networks. Neural networks, 4 0 (2): 0 251--257, 1991

    1991

  44. [52]

    Multilayer feedforward networks are universal approximators

    Kurt Hornik, Maxwell Stinchcombe, and Halbert White. Multilayer feedforward networks are universal approximators. Neural networks, 2 0 (5): 0 359--366, 1989

    1989

  45. [53]

    Stgat: Modeling spatial-temporal interactions for human trajectory prediction

    Yingfan Huang, Huikun Bi, Zhaoxin Li, Tianlu Mao, and Zhaoqi Wang. Stgat: Modeling spatial-temporal interactions for human trajectory prediction. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6272--6281, 2019

    2019

  46. [54]

    A continuum theory for the flow of pedestrians

    Roger L Hughes. A continuum theory for the flow of pedestrians. Transportation Research Part B: Methodological, 36 0 (6): 0 507--535, 2002

    2002

  47. [55]

    Interpretable self-aware neural networks for robust trajectory prediction

    Masha Itkina and Mykel Kochenderfer. Interpretable self-aware neural networks for robust trajectory prediction. In Conference on Robot Learning, pages 606--617. PMLR, 2023

    2023

  48. [56]

    Discrete residual flow for probabilistic pedestrian behavior prediction

    Ajay Jain, Sergio Casas, Renjie Liao, Yuwen Xiong, Song Feng, Sean Segal, and Raquel Urtasun. Discrete residual flow for probabilistic pedestrian behavior prediction. In Conference on Robot Learning, pages 407--419. PMLR, 2020

    2020

  49. [57]

    The material point method for simulating continuum materials

    Chenfanfu Jiang, Craig Schroeder, Joseph Teran, Alexey Stomakhin, and Andrew Selle. The material point method for simulating continuum materials. In Acm siggraph 2016 courses, pages 1--52. 2016

    2016

  50. [58]

    Deepcrowd: A deep model for large-scale citywide crowd density and flow prediction

    Renhe Jiang, Zekun Cai, Zhaonan Wang, Chuang Yang, Zipei Fan, Quanjun Chen, Kota Tsubouchi, Xuan Song, and Ryosuke Shibasaki. Deepcrowd: A deep model for large-scale citywide crowd density and flow prediction. IEEE Transactions on Knowledge and Data Engineering, 35 0 (1): 0 27...

    2021

  51. [59]

    Crowd behavior recognition using dense trajectories

    Muhammad Rizwan Khokher, Abdesselam Bouzerdoum, and Son Lam Phung. Crowd behavior recognition using dense trajectories. In 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pages 1--7. IEEE, 2014

    2014

  52. [60]

    On neural differential equations

    P Kidger. On neural differential equations. PhD thesis, University of Oxford, 2021

    2021

  53. [61]

    Brvo: Predicting pedestrian trajectories using velocity-space reasoning

    Sujeong Kim, Stephen J Guy, Wenxi Liu, David Wilkie, Rynson WH Lau, Ming C Lin, and Dinesh Manocha. Brvo: Predicting pedestrian trajectories using velocity-space reasoning. The International Journal of Robotics Research, 34 0 (2): 0 201--217, 2015

    2015

  54. [62]

    Auto-encoding variational bayes

    Diederik P Kingma. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013

    2013 arXiv

  55. [63]

    Semi-supervised classification with graph convolutional networks

    Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016

    2016 arXiv

  56. [64]

    Activity forecasting

    Kris M Kitani, Brian D Ziebart, James Andrew Bagnell, and Martial Hebert. Activity forecasting. In Computer Vision--ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part IV 12, pages 201--214. Springer, 2012

    2012

  57. [65]

    Crowd behavior analysis: A review where physics meets biology

    Ven Jyn Kok, Mei Kuan Lim, and Chee Seng Chan. Crowd behavior analysis: A review where physics meets biology. Neurocomputing, 177: 0 342--362, 2016

    2016

  58. [66]

    Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks

    Vineet Kosaraju, Amir Sadeghian, Roberto Mart \' n-Mart \' n, Ian Reid, Hamid Rezatofighi, and Silvio Savarese. Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks. Advances in neural information processing systems, 32, 2019

    2019

  59. [67]

    Handwritten digit recognition with a back-propagation network

    Yann LeCun, Bernhard Boser, John Denker, Donnie Henderson, Richard Howard, Wayne Hubbard, and Lawrence Jackel. Handwritten digit recognition with a back-propagation network. Advances in neural information processing systems, 2, 1989

    1989

  60. [68]

    Muse-vae: Multi-scale vae for environment-aware long term trajectory prediction

    Mihee Lee, Samuel S Sohn, Seonghyeon Moon, Sejong Yoon, Mubbasir Kapadia, and Vladimir Pavlovic. Muse-vae: Multi-scale vae for environment-aware long term trajectory prediction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2221--2...

    2022

  61. [69]

    Crowds by example

    Alon Lerner, Yiorgos Chrysanthou, and Dani Lischinski. Crowds by example. In Computer graphics forum, volume 26, pages 655--664. Wiley Online Library, 2007

    2007

  62. [70]

    Graph-based spatial transformer with memory replay for multi-future pedestrian trajectory prediction

    Lihuan Li, Maurice Pagnucco, and Yang Song. Graph-based spatial transformer with memory replay for multi-future pedestrian trajectory prediction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2231--2241, 2022

    2022

  63. [71]

    A deep spatiotemporal perspective for understanding crowd behavior

    Yuke Li. A deep spatiotemporal perspective for understanding crowd behavior. IEEE Transactions on multimedia, 20 0 (12): 0 3289--3297, 2018

    2018

  64. [72]

    Ptp-stgcn: pedestrian trajectory prediction based on a spatio-temporal graph convolutional neural network

    Jing Lian, Weiwei Ren, Linhui Li, Yafu Zhou, and Bin Zhou. Ptp-stgcn: pedestrian trajectory prediction based on a spatio-temporal graph convolutional neural network. Applied Intelligence, 53 0 (3): 0 2862--2878, 2023

    2023

  65. [73]

    Peeking into the future: Predicting future person activities and locations in videos

    Junwei Liang, Lu Jiang, Juan Carlos Niebles, Alexander G Hauptmann, and Li Fei-Fei. Peeking into the future: Predicting future person activities and locations in videos. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5725--5734, 2019

    2019

  66. [74]

    Progressive pretext task learning for human trajectory prediction

    Xiaotong Lin, Tianming Liang, Jianhuang Lai, and Jian-Fang Hu. Progressive pretext task learning for human trajectory prediction. In European Conference on Computer Vision, pages 197--214. Springer, 2024

    2024

  67. [75]

    Intention-aware denoising diffusion model for trajectory prediction

    Chen Liu, Shibo He, Haoyu Liu, and Jiming Chen. Intention-aware denoising diffusion model for trajectory prediction. IEEE Transactions on Intelligent Transportation Systems, 2025

    2025

  68. [76]

    Multimodal-semantic context-aware graph neural network for group activity recognition

    Tianshan Liu, Rui Zhao, and Kin-Man Lam. Multimodal-semantic context-aware graph neural network for group activity recognition. In 2021 IEEE International Conference on Multimedia and Expo (ICME), pages 1--6. IEEE, 2021

    2021

  69. [77]

    Visual-semantic graph neural network with pose-position attentive learning for group activity recognition

    Tianshan Liu, Rui Zhao, Kin-Man Lam, and Jun Kong. Visual-semantic graph neural network with pose-position attentive learning for group activity recognition. Neurocomputing, 491: 0 217--231, 2022 a

    2022

  70. [78]

    Attention-aware social graph transformer networks for stochastic trajectory prediction

    Yao Liu, Binghao Li, Xianzhi Wang, Claude Sammut, and Lina Yao. Attention-aware social graph transformer networks for stochastic trajectory prediction. IEEE Transactions on Knowledge and Data Engineering, 2024

    2024

  71. [79]

    Knowledge-aware graph transformer for pedestrian trajectory prediction

    Yu Liu, Yuexin Zhang, Kunming Li, Yongliang Qiao, Stewart Worrall, You-Fu Li, and He Kong. Knowledge-aware graph transformer for pedestrian trajectory prediction. In 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), pages 4360--4366. IEEE, 2023

    2023

  72. [80]

    Video swin transformer

    Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, and Han Hu. Video swin transformer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3202--3211, 2022 b

    2022

  73. [81]

    Graphic-graph-based representation for analyzing people’s high-level interactions in crowds

    Francesco Longobardi and Daniel Riccio. Graphic-graph-based representation for analyzing people’s high-level interactions in crowds. In 2024 IEEE International Conference on Image Processing (ICIP), pages 868--874. IEEE, 2024

    2024

  74. [82]

    Agent-based human behavior modeling for crowd simulation

    Linbo Luo, Suiping Zhou, Wentong Cai, Malcolm Yoke Hean Low, Feng Tian, Yongwei Wang, Xian Xiao, and Dan Chen. Agent-based human behavior modeling for crowd simulation. Computer Animation and Virtual Worlds, 19 0 (3-4): 0 271--281, 2008

    2008

  75. [83]

    Introduction to gaussian processes

    David JC MacKay et al. Introduction to gaussian processes. NATO ASI series F computer and systems sciences, 168: 0 133--166, 1998

    1998

  76. [84]

    Deep residual network with subclass discriminant analysis for crowd behavior recognition

    Bappaditya Mandal, Jiri Fajtl, Vasileios Argyriou, Dorothy Monekosso, and Paolo Remagnino. Deep residual network with subclass discriminant analysis for crowd behavior recognition. In 2018 25th IEEE International Conference on Image Processing (ICIP), pages 938--942. IEEE, 2018

    2018

  77. [85]

    It is not the journey but the destination: Endpoint conditioned trajectory prediction

    Karttikeya Mangalam, Harshayu Girase, Shreyas Agarwal, Kuan-Hui Lee, Ehsan Adeli, Jitendra Malik, and Adrien Gaidon. It is not the journey but the destination: Endpoint conditioned trajectory prediction. In European Conference on Computer Vision, pages 759--776. Springer, 2020

    2020

  78. [86]

    Leapfrog diffusion model for stochastic trajectory prediction

    Weibo Mao, Chenxin Xu, Qi Zhu, Siheng Chen, and Yanfeng Wang. Leapfrog diffusion model for stochastic trajectory prediction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5517--5526, 2023

    2023

  79. [87]

    A new approach to dominant motion pattern recognition at the macroscopic crowd level

    Franjo Matkovic, Marina Ivasic-Kos, and Slobodan Ribaric. A new approach to dominant motion pattern recognition at the macroscopic crowd level. Engineering applications of artificial intelligence, 116: 0 105387, 2022

    2022

  80. [88]

    Pi-neugode: Physics-informed graph neural ordinary differential equations for spatiotemporal trajectory prediction

    Zhaobin Mo, Yongjie Fu, and Xuan Di. Pi-neugode: Physics-informed graph neural ordinary differential equations for spatiotemporal trajectory prediction. In Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, pages 1418--1426, 2024

    2024

  81. [89]

    Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction

    Abduallah Mohamed, Kun Qian, Mohamed Elhoseiny, and Christian Claudel. Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14424-...

    2020

  82. [90]

    Dag-net: Double attentive graph neural network for trajectory forecasting

    Alessio Monti, Alessia Bertugli, Simone Calderara, and Rita Cucchiara. Dag-net: Double attentive graph neural network for trajectory forecasting. In 2020 25th international conference on pattern recognition (ICPR), pages 2551--2558. IEEE, 2021

    2020

  83. [91]

    The group and crowd analysis interdisciplinary challenge

    Vittorio Murino, Marco Cristani, Shishir Shah, and Silvio Savarese. The group and crowd analysis interdisciplinary challenge. In Group and Crowd Behavior for Computer Vision, pages 1--11. Elsevier, 2017

    2017

  84. [92]

    Convolutional neural network for trajectory prediction

    Nishant Nikhil and Brendan Tran Morris. Convolutional neural network for trajectory prediction. In Proceedings of the European Conference on Computer Vision (ECCV) Workshops, pages 186--196, 2018

    2018

  85. [93]

    You'll never walk alone: Modeling social behavior for multi-target tracking

    Stefano Pellegrini, Andreas Ess, Konrad Schindler, and Luc Van Gool. You'll never walk alone: Modeling social behavior for multi-target tracking. In 2009 IEEE 12th international conference on computer vision, pages 261--268. IEEE, 2009

    2009

  86. [94]

    G tv-l1 optical flow estimation image process

    Javier S \'a nchez P \'e rez, Enric Meinhardt-Llopis, and Gabriele Facciolo. G tv-l1 optical flow estimation image process. On Line, 2013: 0 137--150, 2013

    2013

  87. [95]

    Crowd behavior detection: leveraging video swin transformer for crowd size and violence level analysis

    Marwa Qaraqe, Yin David Yang, Elizabeth B Varghese, Emrah Basaran, and Almiqdad Elzein. Crowd behavior detection: leveraging video swin transformer for crowd size and violence level analysis. Applied Intelligence, 54 0 (21): 0 10709--10730, 2024

    2024

  88. [96]

    Autonomous vehicles that interact with pedestrians: A survey of theory and practice

    Amir Rasouli and John K Tsotsos. Autonomous vehicles that interact with pedestrians: A survey of theory and practice. IEEE transactions on intelligent transportation systems, 21 0 (3): 0 900--918, 2019

    2019

  89. [97]

    Real-time crowd behavior recognition in surveillance videos based on deep learning methods

    Fariba Rezaei and Mehran Yazdi. Real-time crowd behavior recognition in surveillance videos based on deep learning methods. Journal of Real-Time Image Processing, 18 0 (5): 0 1669--1679, 2021

    2021

  90. [98]

    Scene compliant trajectory forecast with agent-centric spatio-temporal grids

    Daniela Ridel, Nachiket Deo, Denis Wolf, and Mohan Trivedi. Scene compliant trajectory forecast with agent-centric spatio-temporal grids. IEEE Robotics and Automation Letters, 5 0 (2): 0 2816--2823, 2020

    2020

  91. [99]

    Learning social etiquette: Human trajectory understanding in crowded scenes

    Alexandre Robicquet, Amir Sadeghian, Alexandre Alahi, and Silvio Savarese. Learning social etiquette: Human trajectory understanding in crowded scenes. In Computer Vision--ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part V...

    2016

  92. [100]

    Parallel distributed processing, volume 1: Explorations in the microstructure of cognition: Foundations

    David E Rumelhart, James L McClelland, PDP Research Group, et al. Parallel distributed processing, volume 1: Explorations in the microstructure of cognition: Foundations. The MIT press, 1986

    1986

  93. [101]

    Trajnet: Towards a benchmark for human trajectory prediction

    Amir Sadeghian, Vineet Kosaraju, Agrim Gupta, Silvio Savarese, and Alexandre Alahi. Trajnet: Towards a benchmark for human trajectory prediction. arXiv preprint, 2018

    2018

  94. [102]

    Sophie: An attentive gan for predicting paths compliant to social and physical constraints

    Amir Sadeghian, Vineet Kosaraju, Ali Sadeghian, Noriaki Hirose, Hamid Rezatofighi, and Silvio Savarese. Sophie: An attentive gan for predicting paths compliant to social and physical constraints. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognit...

    2019

  95. [103]

    Physics constrained pedestrian trajectory prediction with probability quantification

    Haifeng Sang, Jinyu Wang, Quankai Liu, Wangxing Chen, and Zishan Zhao. Physics constrained pedestrian trajectory prediction with probability quantification. Expert Systems with Applications, 255: 0 124743, 2024

    2024

  96. [104]

    Crowd behavior recognition for video surveillance

    Shobhit Saxena, Fran c ois Br \'e mond, Monnique Thonnat, and Ruihua Ma. Crowd behavior recognition for video surveillance. In Advanced Concepts for Intelligent Vision Systems: 10th International Conference, ACIVS 2008, Juan-les-Pins, France, October 20-24, 2008. Proceedings 1...

    2008

  97. [105]

    Distilling free-form natural laws from experimental data

    Michael Schmidt and Hod Lipson. Distilling free-form natural laws from experimental data. science, 324 0 (5923): 0 81--85, 2009

    2009

  98. [106]

    A review of trajectory prediction methods for the vulnerable road user

    Erik Schuetz and Fabian B Flohr. A review of trajectory prediction methods for the vulnerable road user. Robotics, 13 0 (1): 0 1, 2023

    2023

  99. [107]

    Introvert: Human trajectory prediction via conditional 3d attention

    Nasim Shafiee, Taskin Padir, and Ehsan Elhamifar. Introvert: Human trajectory prediction via conditional 3d attention. In Proceedings of the IEEE/cvf Conference on Computer Vision and Pattern recognition, pages 16815--16825, 2021

    2021

  100. [108]

    Scene-independent group profiling in crowd

    Jing Shao, Chen Change Loy, and Xiaogang Wang. Scene-independent group profiling in crowd. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2219--2226, 2014

    2014

  101. [109]

    Deeply learned attributes for crowded scene understanding

    Jing Shao, Kai Kang, Chen Change Loy, and Xiaogang Wang. Deeply learned attributes for crowded scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4657--4666, 2015

    2015

  102. [110]

    Slicing convolutional neural network for crowd video understanding

    Jing Shao, Chen-Change Loy, Kai Kang, and Xiaogang Wang. Slicing convolutional neural network for crowd video understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5620--5628, 2016

    2016

  103. [111]

    Sgcn: Sparse graph convolution network for pedestrian trajectory prediction

    Liushuai Shi, Le Wang, Chengjiang Long, Sanping Zhou, Mo Zhou, Zhenxing Niu, and Gang Hua. Sgcn: Sparse graph convolution network for pedestrian trajectory prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8994--9003, 2021

    2021

  104. [112]

    Trajectory unified transformer for pedestrian trajectory prediction

    Liushuai Shi, Le Wang, Sanping Zhou, and Gang Hua. Trajectory unified transformer for pedestrian trajectory prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9675--9684, 2023

    2023

  105. [113]

    Pedestrian trajectory prediction in extremely crowded scenarios

    Xiaodan Shi, Xiaowei Shao, Zhiling Guo, Guangming Wu, Haoran Zhang, and Ryosuke Shibasaki. Pedestrian trajectory prediction in extremely crowded scenarios. Sensors, 19 0 (5): 0 1223, 2019

    2019

  106. [114]

    Convolutional lstm network: A machine learning approach for precipitation nowcasting

    Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo. Convolutional lstm network: A machine learning approach for precipitation nowcasting. Advances in neural information processing systems, 28, 2015

    2015

  107. [115]

    Adaptive feature learning cnn for behavior recognition in crowd scene

    Aliyu Nuhu Shuaibu, Aamir Saeed Malik, and Ibrahima Faye. Adaptive feature learning cnn for behavior recognition in crowd scene. In 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pages 357--361. IEEE, 2017

    2017

  108. [116]

    A review of deep learning-based methods for pedestrian trajectory prediction

    Bogdan Ilie Sighencea, Rareș Ion Stanciu, and C a t a lin Daniel C a leanu. A review of deep learning-based methods for pedestrian trajectory prediction. Sensors, 21 0 (22): 0 7543, 2021

    2021

  109. [117]

    D-stgcn: Dynamic pedestrian trajectory prediction using spatio-temporal graph convolutional networks

    Bogdan Ilie Sighencea, Ion Rareș Stanciu, and C a t a lin Daniel C a leanu. D-stgcn: Dynamic pedestrian trajectory prediction using spatio-temporal graph convolutional networks. Electronics, 12 0 (3): 0 611, 2023

    2023

  110. [118]

    Very deep convolutional networks for large-scale image recognition

    K Simonyan and A Zisserman. Very deep convolutional networks for large-scale image recognition. In 3rd International Conference on Learning Representations (ICLR 2015). Computational and Biological Learning Society, 2015

    2015

  111. [119]

    Steerbench: a benchmark suite for evaluating steering behaviors

    Shawn Singh, Mubbasir Kapadia, Petros Faloutsos, and Glenn Reinman. Steerbench: a benchmark suite for evaluating steering behaviors. Comput. Animat. Virtual Worlds, 20 0 (5‐6): 0 533–548, September 2009. ISSN 1546-4261

    2009

  112. [120]

    Deep unsupervised learning using nonequilibrium thermodynamics

    Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learning, pages 2256--2265. PMLR, 2015

    2015

  113. [121]

    Learning structured output representation using deep conditional generative models

    Kihyuk Sohn, Honglak Lee, and Xinchen Yan. Learning structured output representation using deep conditional generative models. Advances in neural information processing systems, 28, 2015

    2015

  114. [122]

    Identifying behaviors in crowd scenes using stability analysis for dynamical systems

    Berkan Solmaz, Brian E Moore, and Mubarak Shah. Identifying behaviors in crowd scenes using stability analysis for dynamical systems. IEEE transactions on pattern analysis and machine intelligence, 34 0 (10): 0 2064--2070, 2012

    2012

  115. [123]

    Pedestrian trajectory prediction based on deep convolutional lstm network

    Xiao Song, Kai Chen, Xu Li, Jinghan Sun, Baocun Hou, Yong Cui, Baochang Zhang, Gang Xiong, and Zilie Wang. Pedestrian trajectory prediction based on deep convolutional lstm network. IEEE Transactions on Intelligent Transportation Systems, 22 0 (6): 0 3285--3302, 2020

    2020

  116. [124]

    The large-scale crowd behavior perception based on spatio-temporal viscous fluid field

    Hang Su, Hua Yang, Shibao Zheng, Yawen Fan, and Sha Wei. The large-scale crowd behavior perception based on spatio-temporal viscous fluid field. IEEE Transactions on Information Forensics and security, 8 0 (10): 0 1575--1589, 2013

    2013

  117. [125]

    Crowd scene understanding with coherent recurrent neural networks

    Hang Su, Yinpeng Dong, Jun Zhu, Haibin Ling, and Bo Zhang. Crowd scene understanding with coherent recurrent neural networks. In IJCAI, volume 1, page 2, 2016

    2016

  118. [126]

    Crowd behavior analysis: A survey

    HY Swathi, G Shivakumar, and HS Mohana. Crowd behavior analysis: A survey. In 2017 international conference on recent advances in electronics and communication technology (ICRAECT), pages 169--178. IEEE, 2017

    2017

  119. [127]

    Enhancing pedestrian trajectory prediction with crowd trip information

    Rei Tamaru, Pei Li, and Bin Ran. Enhancing pedestrian trajectory prediction with crowd trip information. arXiv preprint arXiv:2409.15224, 2024

    2024 arXiv

  120. [128]

    Design and analysis of efficient attention in transformers for social group activity recognition

    Masato Tamura. Design and analysis of efficient attention in transformers for social group activity recognition. International Journal of Computer Vision, pages 1--20, 2024

    2024

  121. [129]

    Hunting group clues with transformers for social group activity recognition

    Masato Tamura, Rahul Vishwakarma, and Ravigopal Vennelakanti. Hunting group clues with transformers for social group activity recognition. In European Conference on Computer Vision, pages 19--35. Springer, 2022

    2022

  122. [130]

    Raft: Recurrent all-pairs field transforms for optical flow

    Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow. In Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part II 16, pages 402--419. Springer, 2020

    2020

  123. [131]

    A literature review on video analytics of crowded scenes

    Myo Thida, Yoke Leng Yong, Pau Climent-P \'e rez, How-lung Eng, and Paolo Remagnino. A literature review on video analytics of crowded scenes. Intelligent Multimedia Surveillance: Current Trends and Research, pages 17--36, 2013

    2013

  124. [132]

    Long-range order in a two-dimensional dynamical xy model: how birds fly together

    John Toner and Yuhai Tu. Long-range order in a two-dimensional dynamical xy model: how birds fly together. Physical review letters, 75 0 (23): 0 4326, 1995

    1995

  125. [133]

    Learning spatiotemporal features with 3d convolutional networks

    Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri. Learning spatiotemporal features with 3d convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 4489--4497, 2015

    2015

  126. [134]

    Goal-driven long-term trajectory prediction

    Hung Tran, Vuong Le, and Truyen Tran. Goal-driven long-term trajectory prediction. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 796--805, 2021

    2021

  127. [135]

    Two stream model for crowd video classification

    Habib Ullah, Sultan Daud Khan, Mohib Ullah, Faouzi Alaya Cheikh, and Muhammad Uzair. Two stream model for crowd video classification. In 2019 8th european workshop on visual information processing (EUVIP), pages 93--98. IEEE, 2019

    2019

  128. [136]

    Multi-feature-based crowd video modeling for visual event detection

    Habib Ullah, Ihtesham Ul Islam, Mohib Ullah, Muhammad Afaq, Sultan Daud Khan, and Javed Iqbal. Multi-feature-based crowd video modeling for visual event detection. Multimedia Systems, 27: 0 589--597, 2021

    2021

  129. [137]

    Deep neural network model for group activity recognition using contextual relationship

    SA Vahora and NC Chauhan. Deep neural network model for group activity recognition using contextual relationship. Engineering Science and Technology, an International Journal, 22 0 (1): 0 47--54, 2019

    2019

  130. [138]

    Reciprocal velocity obstacles for real-time multi-agent navigation

    Jur Van den Berg, Ming Lin, and Dinesh Manocha. Reciprocal velocity obstacles for real-time multi-agent navigation. In 2008 IEEE international conference on robotics and automation, pages 1928--1935. Ieee, 2008

    2008

  131. [139]

    Extreme-density crowd simulation: combining agents with smoothed particle hydrodynamics

    Wouter van Toll, C \'e dric Braga, Barbara Solenthaler, and Julien Pettr \'e . Extreme-density crowd simulation: combining agents with smoothed particle hydrodynamics. In Proceedings of the 13th ACM SIGGRAPH Conference on Motion, Interaction and Games, pages 1--10, 2020

    2020

  132. [140]

    Attention is all you need

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, ukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017

    2017

  133. [141]

    Analysis of deep learning in real-world applications: Challenges and progress

    Vivek Velayutham, Sanjay Kumar, Avinash Kumar, Shrinwantu Raha, and Gonesh Chandra Saha. Analysis of deep learning in real-world applications: Challenges and progress. Tuijin Jishu/Journal of Propulsion Technology, 44 0 (2): 0 2023

    2023

  134. [142]

    Graph attention networks

    Petar Veli c kovi \'c , Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li \`o , and Yoshua Bengio. Graph attention networks. In International Conference on Learning Representations, 2018

    2018

  135. [143]

    A generalized loss function for crowd counting and localization

    Jia Wan, Ziquan Liu, and Antoni B Chan. A generalized loss function for crowd counting and localization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1974--1983, 2021

    1974

  136. [144]

    Graphtcn: Spatio-temporal interaction modeling for human trajectory prediction

    Chengxin Wang, Shaofeng Cai, and Gary Tan. Graphtcn: Spatio-temporal interaction modeling for human trajectory prediction. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 3450--3459, 2021

    2021

  137. [145]

    Globally continuous and non-markovian crowd activity analysis from videos

    He Wang and Carol O'Sullivan. Globally continuous and non-markovian crowd activity analysis from videos. In The European Conference on Computer Vision (ECCV), 2016

    2016

  138. [146]

    Path patterns: Analyzing and comparing real and simulated crowds

    He Wang, Jan Ondřej, and Carol O'Sullivan. Path patterns: Analyzing and comparing real and simulated crowds. In ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (ACM I3D), pages 49--57, 2016

    2016

  139. [147]

    Trending paths: A new semantic-level metric for comparing simulated and real crowd data

    He Wang, Jan Ondřej, and Carol O'Sullivan. Trending paths: A new semantic-level metric for comparing simulated and real crowd data. IEEE Transactions on Visualization and Computer Graphics (TVCG), 2017 a

    2017

  140. [148]

    Trinary-projection trees for approximate nearest neighbor search

    Jingdong Wang, Naiyan Wang, You Jia, Jian Li, Gang Zeng, Hongbin Zha, and Xian-Sheng Hua. Trinary-projection trees for approximate nearest neighbor search. IEEE transactions on pattern analysis and machine intelligence, 36 0 (2): 0 388--403, 2013

    2013

  141. [149]

    Neural differential constraint-based pedestrian trajectory prediction model in ego-centric perspective

    Jinyu Wang, Haifeng Sang, Quankai Liu, Wangxing Chen, and Zishan Zhao. Neural differential constraint-based pedestrian trajectory prediction model in ego-centric perspective. Engineering Applications of Artificial Intelligence, 133: 0 107993, 2024

    2024

  142. [150]

    Recurrent modeling of interaction context for collective activity recognition

    Minsi Wang, Bingbing Ni, and Xiaokang Yang. Recurrent modeling of interaction context for collective activity recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3048--3056, 2017 b

    2017

  143. [151]

    Xiaogang Wang, Xiaoxu Ma, and W.E.L. Grimson. Unsupervised activity perception in crowded and complicated scenes using hierarchical bayesian models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31 0 (3): 0 539--555, 2009. doi:10.1109/TPAMI.2008.87

    2009 doi

  144. [152]

    Trajectory analysis and semantic region modeling using nonparametric hierarchical bayesian models

    Xiaogang Wang, Keng Teck Ma, Gee-Wah Ng, and W Eric L Grimson. Trajectory analysis and semantic region modeling using nonparametric hierarchical bayesian models. International journal of computer vision, 95: 0 287--312, 2011

    2011

  145. [153]

    A very deep two-stream network for crowd type recognition

    Xinlei Wei, Junping Du, Zhe Xue, Meiyu Liang, Yue Geng, Xin Xu, and JangMyung Lee. A very deep two-stream network for crowd type recognition. Neurocomputing, 396: 0 522--533, 2020

    2020

  146. [154]

    Understanding crowd behaviour

    Nanda Wijermans. Understanding crowd behaviour. PhD thesis, University of Groningen, 2011

    2011

  147. [155]

    Motion latent diffusion for stochastic trajectory prediction

    Weishang Wu and Xiaoheng Deng. Motion latent diffusion for stochastic trajectory prediction. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 6665--6669. IEEE, 2024

    2024

  148. [156]

    A comprehensive survey on graph neural networks

    Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, 32 0 (1): 0 4--24, 2020

    2020

  149. [157]

    Socialcvae: Predicting pedestrian trajectory via interaction conditioned latents

    Wei Xiang, YIN Haoteng, He Wang, and Xiaogang Jin. Socialcvae: Predicting pedestrian trajectory via interaction conditioned latents. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 6216--6224, 2024

    2024

  150. [158]

    Remember intentions: Retrospective-memory-based trajectory prediction

    Chenxin Xu, Weibo Mao, Wenjun Zhang, and Siheng Chen. Remember intentions: Retrospective-memory-based trajectory prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6488--6497, 2022 a

    2022

  151. [159]

    Collision-free lstm for human trajectory prediction

    Kaiping Xu, Zheng Qin, Guolong Wang, Kai Huang, Shuxiong Ye, and Huidi Zhang. Collision-free lstm for human trajectory prediction. In MultiMedia Modeling: 24th International Conference, MMM 2018, Bangkok, Thailand, February 5-7, 2018, Proceedings, Part I 24, pages 106--116. Sp...

    2018

  152. [160]

    Socialvae: Human trajectory prediction using timewise latents

    Pei Xu, Jean-Bernard Hayet, and Ioannis Karamouzas. Socialvae: Human trajectory prediction using timewise latents. In European Conference on Computer Vision, pages 511--528. Springer, 2022 b

    2022

  153. [161]

    Encoding crowd interaction with deep neural network for pedestrian trajectory prediction

    Yanyu Xu, Zhixin Piao, and Shenghua Gao. Encoding crowd interaction with deep neural network for pedestrian trajectory prediction. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5275--5284, 2018 b

    2018

  154. [162]

    S-cgru: An efficient model for pedestrian trajectory prediction

    Zhenwei Xu, Qing Yu, Wushouer Slamu, Yaoyong Zhou, and Zhida Liu. S-cgru: An efficient model for pedestrian trajectory prediction. In International Conference on Neural Information Processing, pages 244--259. Springer, 2023

    2023

  155. [163]

    Ss-lstm: A hierarchical lstm model for pedestrian trajectory prediction

    Hao Xue, Du Q Huynh, and Mark Reynolds. Ss-lstm: A hierarchical lstm model for pedestrian trajectory prediction. In 2018 IEEE winter conference on applications of computer vision (WACV), pages 1186--1194. IEEE, 2018

    2018

  156. [164]

    Who are you with and where are you going? In CVPR 2011, pages 1345--1352

    Kota Yamaguchi, Alexander C Berg, Luis E Ortiz, and Tamara L Berg. Who are you with and where are you going? In CVPR 2011, pages 1345--1352. IEEE, 2011

    2011

  157. [165]

    Crowd video captioning

    Liqi Yan, Mingjian Zhu, and Changbin Yu. Crowd video captioning. arXiv preprint arXiv:1911.05449, 2019

    1911 arXiv

  158. [166]

    Spatial temporal graph convolutional networks for skeleton-based action recognition

    Sijie Yan, Yuanjun Xiong, and Dahua Lin. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence, volume 32, 2018

    2018

  159. [167]

    A review on crowd simulation and modeling

    Shanwen Yang, Tianrui Li, Xun Gong, Bo Peng, and Jie Hu. A review on crowd simulation and modeling. Graphical Models, 111: 0 101081, 2020

    2020

  160. [168]

    Following in the footsteps: Predicting human trajectories using motion pattern memory

    Yuxin Yang, Pengfei Zhu, Mengshi Qi, and Huadong Ma. Following in the footsteps: Predicting human trajectories using motion pattern memory. In Proceedings of the 6th ACM International Conference on Multimedia in Asia, pages 1--7, 2024

    2024

  161. [169]

    Spatio-temporal graph transformer networks for pedestrian trajectory prediction

    Cunjun Yu, Xiao Ma, Jiawei Ren, Haiyu Zhao, and Shuai Yi. Spatio-temporal graph transformer networks for pedestrian trajectory prediction. In Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part XII 16, pages 507--523. Sprin...

    2020

  162. [170]

    Pedestrian trajectory prediction using spatio-temporal vae

    Qing Yu, Zhenwei Xu, Yaoyong Zhou, Zhida Liu, and Wushouer Silamu. Pedestrian trajectory prediction using spatio-temporal vae. In Chinese Conference on Pattern Recognition and Computer Vision (PRCV), pages 297--311. Springer, 2024

    2024

  163. [171]

    Agentformer: Agent-aware transformers for socio-temporal multi-agent forecasting

    Ye Yuan, Xinshuo Weng, Yanglan Ou, and Kris M Kitani. Agentformer: Agent-aware transformers for socio-temporal multi-agent forecasting. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9813--9823, 2021

    2021

  164. [172]

    Human trajectory prediction via neural social physics

    Jiangbei Yue, Dinesh Manocha, and He Wang. Human trajectory prediction via neural social physics. In European conference on computer vision, pages 376--394. Springer, 2022

    2022

  165. [173]

    Human trajectory forecasting with explainable behavioral uncertainty

    Jiangbei Yue, Dinesh Manocha, and He Wang. Human trajectory forecasting with explainable behavioral uncertainty. arXiv preprint arXiv:2307.01817, 2023

    2023 arXiv

  166. [174]

    Human motion prediction under unexpected perturbation

    Jiangbei Yue, Baiyi Li, Julien Pettr \'e , Armin Seyfried, and He Wang. Human motion prediction under unexpected perturbation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1501--1511, 2024

    2024

  167. [175]

    Pedestrian trajectory prediction with convolutional neural networks

    Simone Zamboni, Zekarias Tilahun Kefato, Sarunas Girdzijauskas, Christoffer Nor \'e n, and Laura Dal Col. Pedestrian trajectory prediction with convolutional neural networks. Pattern Recognition, 121: 0 108252, 2022

    2022

  168. [176]

    Adaptive deconvolutional networks for mid and high level feature learning

    Matthew D Zeiler, Graham W Taylor, and Rob Fergus. Adaptive deconvolutional networks for mid and high level feature learning. In 2011 international conference on computer vision, pages 2018--2025. IEEE, 2011

    2011

  169. [177]

    Crowd analysis: a survey

    Beibei Zhan, Dorothy N Monekosso, Paolo Remagnino, Sergio A Velastin, and Li-Qun Xu. Crowd analysis: a survey. Machine Vision and Applications, 19: 0 345--357, 2008

    2008

  170. [178]

    Social-iwstcnn: A social interaction-weighted spatio-temporal convolutional neural network for pedestrian trajectory prediction in urban traffic scenarios

    Chi Zhang, Christian Berger, and Marco Dozza. Social-iwstcnn: A social interaction-weighted spatio-temporal convolutional neural network for pedestrian trajectory prediction in urban traffic scenarios. In 2021 IEEE Intelligent Vehicles Symposium (IV), pages 1515--1522. IEEE, 2021

    2021

  171. [179]

    Step attention: Sequential pedestrian trajectory prediction

    Ethan Zhang, Neda Masoud, Mahdi Bandegi, Joseph Lull, and Rajesh K Malhan. Step attention: Sequential pedestrian trajectory prediction. IEEE Sensors Journal, 22 0 (8): 0 8071--8083, 2022

    2022

  172. [180]

    A comprehensive review of deep learning approaches for group activity analysis

    Gang Zhang, Yang Geng, and Zhao G Gong. A comprehensive review of deep learning approaches for group activity analysis. The Visual Computer, 41 0 (3): 0 1733--1755, 2025

    2025

  173. [181]

    Sr-lstm: State refinement for lstm towards pedestrian trajectory prediction

    Pu Zhang, Wanli Ouyang, Pengfei Zhang, Jianru Xue, and Nanning Zheng. Sr-lstm: State refinement for lstm towards pedestrian trajectory prediction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12085--12094, 2019

    2019

  174. [182]

    Pedestrian trajectory prediction with mlp-social-gru

    Yanbo Zhang and Liying Zheng. Pedestrian trajectory prediction with mlp-social-gru. In Proceedings of the 2021 13th International Conference on Machine Learning and Computing, pages 368--372, 2021

    2021

  175. [183]

    A role-dependent data-driven approach for high-density crowd behavior modeling

    Mingbi Zhao, Jinghui Zhong, and Wentong Cai. A role-dependent data-driven approach for high-density crowd behavior modeling. ACM Transactions on Modeling and Computer Simulation (TOMACS), 28 0 (4): 0 1--25, 2018

    2018

  176. [184]

    Understanding collective crowd behaviors: Learning a mixture model of dynamic pedestrian-agents

    Bolei Zhou, Xiaogang Wang, and Xiaoou Tang. Understanding collective crowd behaviors: Learning a mixture model of dynamic pedestrian-agents. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 2871--2878. IEEE, 2012

    2012

  177. [185]

    Measuring crowd collectiveness

    Bolei Zhou, Xiaoou Tang, and Xiaogang Wang. Measuring crowd collectiveness. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3049--3056, 2013

    2013

  178. [186]

    Ast-gnn: An attention-based spatio-temporal graph neural network for interaction-aware pedestrian trajectory prediction

    Hao Zhou, Dongchun Ren, Huaxia Xia, Mingyu Fan, Xu Yang, and Hai Huang. Ast-gnn: An attention-based spatio-temporal graph neural network for interaction-aware pedestrian trajectory prediction. Neurocomputing, 445: 0 298--308, 2021

    2021

  179. [187]

    Hydrodynamics-informed neural network for simulating dense crowd motion patterns

    Yanshan Zhou, Pingrui Lai, Jiaqi Yu, Yingjie Xiong, and Hua Yang. Hydrodynamics-informed neural network for simulating dense crowd motion patterns. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 4553--4561, 2024

    2024

  180. [188]

    Dynamic attention-based cvae-gan for pedestrian trajectory prediction

    Zhou Zhou, Gang Huang, Zhaoxin Su, Yongfu Li, and Wei Hua. Dynamic attention-based cvae-gan for pedestrian trajectory prediction. IEEE Robotics and Automation Letters, 8 0 (2): 0 704--711, 2022

    2022

  181. [189]

    Toward multimodal image-to-image translation

    Jun-Yan Zhu, Richard Zhang, Deepak Pathak, Trevor Darrell, Alexei A Efros, Oliver Wang, and Eli Shechtman. Toward multimodal image-to-image translation. Advances in neural information processing systems, 30, 2017

    2017

  182. [190]

    Subclass discriminant analysis

    Manli Zhu and Aleix M Martinez. Subclass discriminant analysis. IEEE transactions on pattern analysis and machine intelligence, 28 0 (8): 0 1274--1286, 2006

    2006

  183. [191]

    Deformable detr: Deformable transformers for end-to-end object detection

    Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai. Deformable detr: Deformable transformers for end-to-end object detection. In International Conference on Learning Representations, 2021

    2021

  184. [192]

    Starnet: Pedestrian trajectory prediction using deep neural network in star topology

    Yanliang Zhu, Deheng Qian, Dongchun Ren, and Huaxia Xia. Starnet: Pedestrian trajectory prediction using deep neural network in star topology. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 8075--8080. IEEE, 2019

    2019

  185. [193]

    V3trans-crowd: A video-based visual transformer for crowd management monitoring

    Yuqi Zuo, Aymen Hamrouni, Hakim Ghazzai, and Yehia Massoud. V3trans-crowd: A video-based visual transformer for crowd management monitoring. In 2023 IEEE International Conference on Smart Mobility (SM), pages 154--159. IEEE, 2023

    2023

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.