Pith. sign in

REVIEW 4 major objections 7 minor 62 references

RPEE-HEADS: A Novel Benchmark for Pedestrian Head Detection in Crowd Videos

T0 review · 4 major / 7 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A new dataset of 109,913 pedestrian-head boxes closes a domain gap in head detection at railway platforms and event entrances, raising mAP from at most 56.9% to 90.8%.

desk verdict A genuinely useful crowd-head dataset, but the headline accuracy gap is likely inflated by a block-level train/test split that leaks near-duplicate frames from the same videos. read the letter →

arxiv 2411.18164 v1 pith:264G6I65 submitted 2024-11-27 cs.CV cs.LG

Mohamad Abubaker , Zubayda Alsadder , Hamed Abdelhaq , Maik Boltes , Ahmed Alia This is my paper · ORCID
classification cs.CVcs.LG
keywords RPEE-Headspedestrianheaddetectioncrowdanalysisobjectbenchmarkrailwayplatformsevententranceshead-sizeRT-DETR
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

The paper introduces RPEE-Heads, a benchmark of 109,913 manually annotated pedestrian-head bounding boxes in 1,886 images sampled from 66 videos of railway platforms and event entrances. Its central claim is that this setting is underrepresented in public head-detection datasets, and that the gap shows up as a large accuracy drop: models trained on FDST, SCUT-HEAD, or Hollywood Heads and tested on RPEE-Heads reach at most 56.9% mean average precision, whereas YOLOv9-E and RT-DETR trained on RPEE-Heads reach 90.7% and 90.8%. The paper also reports an empirical size effect: datasets dominated by heads below about 62 square pixels, such as JHU-CROWD++ and NWPU-Crowd, yield poor detector accuracy. If the claims hold, the dataset gives crowd-safety researchers a domain-specific training and evaluation resource, plus a baseline across eight modern detectors.

What carries the argument

The central object is the RPEE-Heads dataset itself: 1,886 high-resolution frames with normalized bounding-box annotations (class, center x, center y, width, height) for 109,913 visible heads, split by video blocks into 70/15/15 train/validation/test sets to minimize near-duplicate frames between sets. The benchmark machinery also includes five head-size categories (under 62, 72 to 122, 132 to 242, 252 to 322, and above 332 square pixels) and the standard mAP@0.5 metric, which together let the paper attribute performance differences to head scale and to domain mismatch.

What would settle it

Re-annotate a random sample of, say, 200 RPEE-Heads test frames with independent annotators and measure per-box Intersection-over-Union agreement; if agreement falls well below the 0.5 IoU threshold used to score detectors, the reported 90%+ mAP may partly reflect lenient ground truth. A sharper test: have one experienced annotator re-label the entire test set and recompute YOLOv9-E and RT-DETR mAP; a large drop would indicate the original labels carried the headline numbers.

Watch

Extended reading notes

Core claim

RPEE-Heads is built from 66 video recordings: 15 railway-platform videos, 34 music-concert entrance videos, and 17 indoor event-entrance experiments sourced from an open archive. Frames were sampled at intervals chosen to reduce duplicate scenes, and two annotators manually placed bounding boxes around visible head regions, yielding 109,913 boxes over 1,886 frames with an average of 56.2 heads per frame. The paper evaluates Fast R-CNN, Cascade R-CNN, RetinaNet-101, Faster R-CNN, RT-DETR, YOLOv7x, YOLOv8x, and YOLOv9-E at an IoU threshold of 0.5; YOLOv9-E and RT-DETR score 90.7% and 90.8% mAP with 11 ms and 14 ms inference times, respectively. When the top three models are trained from scratch on FDST, SCUT-HEAD Part A, SCUT-HEAD Part B, and Hollywood Heads and evaluated on the RPEE-Heads test set, their mAP falls to at most 56.9%. The authors conclude that specialized datasets like RPEE-Heads are needed for head detection at railway platforms and event entrances, and that small heads, particularly below 62 square pixels, degrade detector performance.

Load-bearing premise

The benchmark's value rests on the assumption that the 109,913 manually placed bounding boxes are accurate and consistent; the paper reports no inter-annotator agreement or error analysis, so if the ground truth is noisy, the reported mAP figures and cross-dataset comparisons are not reliable.

Editorial extensions

If this is right

  • Crowd-management systems at railway stations and event entrances can use RPEE-Heads-trained YOLOv9-E or RT-DETR as a real-time head detector with around 90% mAP and 11–14 ms per frame.
  • The paper gives the community a fixed evaluation suite: eight detectors scored on the same test split with precision, recall, F1, mAP, true/false positives, false negatives, and inference time.
  • The head-size distribution tables let curators check whether a new dataset is dominated by heads below about 62 square pixels, the range the paper finds most damaging to accuracy.
  • Block-wise train/validation/test splits across videos mean the reported scores are less likely to be inflated by near-duplicate frames between training and test.
  • The public release of the labeled data and pretrained models allows the results to be reproduced and extended.

Reading between the lines

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

  • The reported cross-dataset gap is likely a mixture of head-size distribution and viewpoint/background shift; a controlled experiment that matches head-size distributions across datasets while varying camera angle could separate the two effects.
  • Because only visible head regions are annotated and the source videos exist, a natural extension the paper leaves implicit is using temporal continuity from the videos for occlusion-aware tracking, but the released benchmark does not provide track IDs.
  • The small-head result points to overhead, low-resolution cameras as the hard regime; one testable next step is combining RPEE-Heads with super-resolution or scale-adaptive detection heads and measuring mAP for the sub-62-square-pixel bin.
  • If inter-annotator agreement turns out high, the benchmark could also serve as a pretraining set for other dense-crowd domains, not just railway stations and event entrances.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 7 minor

Summary. The paper introduces RPEE-Heads, a pedestrian-head detection benchmark with 109,913 bounding boxes across 1,886 frames from 66 railway-platform and event-entrance videos. It trains eight object detectors (Fast/Faster/Cascade R-CNN, RetinaNet-101, YOLOv7x/8x/9-E, RT-DETR) on this dataset and reports that YOLOv9-E and RT-DETR achieve the highest mAP@0.5 (90.7% and 90.8%). It then trains the top three detectors on public head-detection datasets (FDST, SCUT-HEAD Parts A/B, Hollywood Heads) and evaluates them on the RPEE-Heads test set, reporting substantially lower performance (mAP at most 56.9%). A further analysis relates head size to detection accuracy. The authors conclude that RPEE-Heads is necessary for accurate head detection in railway-platform and event-entrance scenarios and release the dataset and pretrained models at a DOI.

Significance. If the claims hold, RPEE-Heads addresses a real gap: dense-crowd railway and event-entrance scenes are underrepresented in public head-detection benchmarks, and the paper provides a new annotated resource plus a documented comparison of eight detectors. The external-anchor design of the cross-dataset experiment is a strength: models trained on public data and evaluated on RPEE-Heads test a genuine generalization question, and the fixed hyperparameter table makes the within-dataset comparison reproducible in principle. The public release of the data and models is another concrete contribution. However, the significance is conditional on the benchmark-construction evidence: the train/test split procedure and the annotation quality are not yet documented at the level a benchmark paper requires, and the headline performance gap could be materially affected by split leakage. The head-size analysis is correlational and useful, but it is undermined by an internal threshold inconsistency.

major comments (4)
  1. [Section 3.2, Table 2, Section 4.4] The block-wise random split described in Section 3.2 does not prevent the same video (or the same static scene) from contributing blocks to both the training and test sets. This risk is concrete: for the Event Entrances Experiments, frames are sampled every 3 seconds (Table 2), so frames in different sets can be only seconds apart, with nearly identical backgrounds and pedestrian configurations. The paper provides no evidence that blocks from a single video are constrained to a single split. Because the paper's central claim is the large mAP gap between RPEE-trained models (about 90.7-90.8%) and public-dataset-trained models (at most 56.9%, Fig. 8), such near-duplicate leakage would directly bias the comparison in favor of RPEE-trained models. The authors should report the block-to-split assignment at the level of each video, re-run the comparison with a video- or scene-exclusive split, or provide quantitative evidence (e.g., frame-similarity distributions) that training and test frames do not share near-duplicate content.
  2. [Section 3.2, Section 5] The annotation protocol is described only as 'accurate annotations were manually added' by two annotators; the paper reports no inter-annotator agreement, no quality-control protocol, and no error analysis for the 109,913 bounding boxes. With an average of 56.2 heads per image in dense crowds, annotation omissions and box misplacements are plausible and would directly affect every reported mAP and the benchmark's utility for future work. The authors should report an inter-annotator agreement measure (e.g., per-image IoU or box-level agreement on a random subset) and an error analysis stratified by density, head size, and occlusion, or explain why such verification is unnecessary for this dataset.
  3. [Section 4.2, Table 5, Section 4.4] All results are single-run point estimates with no error bars, confidence intervals, or significance tests. The claimed superiority of RT-DETR over YOLOv9-E (90.8% vs 90.7% mAP) is within plausible run-to-run variation, especially for stochastic training; similarly, the cross-dataset gap in Fig. 8 has no variance estimate. The authors should provide at least repeated runs (or a standard error) for the top models and the main cross-dataset comparison, or otherwise temper claims of 'outperform' and 'significant' where the differences are small.
  4. [Section 4.4, Table 6, Section 5] The head-size conclusion is internally inconsistent: the summary states that 'objects or heads occupying less than 36 square pixels' are the problem, while the analysis and Table 6 consistently use a threshold of 'less than 62 pixel2' (and the Conclusion says 'within 62 pixel2'). This is not a formatting issue: the threshold is load-bearing for the paper's third contribution. The authors should correct the threshold and ensure that the text, figures, and tables use the same head-size categories.
minor comments (7)
  1. [Section 3.2] The dataset name is written as 'RPEE-Head dataset' in one place; it should be 'RPEE-Heads' for consistency.
  2. [Section 4.4] 'SCUT-HEAD Par B' is a typo; it should be 'SCUT-HEAD Part B'.
  3. [Figure 4, Table 6] The head-size bins are not contiguous: '02-62' and '72-122' leave the range 63-71 pixel2 unaccounted for, as do the later ranges. Please make the bins exhaustive and non-overlapping, or state explicitly why intermediate sizes are excluded.
  4. [Section 3.2] The random 70/15/15 split is described as 'approximately and randomly' performed, with no random seed or split file referenced. For reproducibility, the exact split (or a seed) should be provided with the dataset.
  5. [Throughout] The abbreviation 'YOLOV8x' appears without a hyphen in several places; it should be 'YOLOv8x' consistently for readability.
  6. [References] Reference [45] contains 'iEEE' and should read 'IEEE'; also, the citation formatting in the reference list is inconsistent in a few entries.
  7. [Section 4.4] The paper does not explicitly state how the public-dataset training splits were chosen and whether the same hyperparameters and input sizes from Table 4 were used for the FDST/SCUT-HEAD/Hollywood models. Please clarify this protocol, since it affects the interpretation of Table 7 and Fig. 8.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the headline generalization claim is anchored to an independent held-out test set, and the paper's self-citations are background references rather than load-bearing derivations.

full rationale

The paper's central empirical claim is that models trained on RPEE-Heads outperform models trained on public datasets when evaluated on the RPEE-Heads test set. This comparison is anchored externally: the public-dataset models are trained on FDST, SCUT-HEAD, or Hollywood Heads and then evaluated on a held-out portion of RPEE-Heads that was not used for training those models. The RPEE-trained models are likewise evaluated on a test split not used for their training. No equation in the paper reduces a reported quantity to a fitted parameter, and no 'prediction' is defined in terms of the data used to fit it. The head-size analysis in Section 4.4 is a correlational empirical study, not a derivation: mAP is measured independently and then related to the observed distribution of head sizes. The manual-annotation process is a data-construction contribution, not a circular step. Self-citations appear in the introduction and related work, but they support background statements about pushing detection and prior algorithms; they do not justify the dataset's validity or the comparative results. The reviewer concern about the block-wise train/test split possibly leaking near-duplicate frames from the same videos is a legitimate data-quality and experimental-design risk, but it is not a case where the paper's result is equivalent to its input by construction. No quoted reduction of the kind required for a circularity finding is present.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new theoretical entities or fitted constants; its claims rest on dataset quality, annotation reliability, and evaluation protocol assumptions.

assumptions (4)
  • domain assumption The 109,913 manual head bounding boxes are accurate and consistent enough to serve as ground truth.
    Invoked in Section 3.2 when annotations are created and used without inter-annotator agreement or quality checks.
  • domain assumption mAP@0.5 with IoU threshold 0.5 is an appropriate evaluation metric for this detection task.
    Section 4.2 states all experiments use IoU 0.5; this choice affects all reported numbers.
  • domain assumption Block-based random split into 70/15/15 prevents significant scene overlap between train and test sets.
    Section 3.2: videos are divided into blocks and split; blocks from the same video can land in different sets, risking leakage.
  • domain assumption Default hyperparameters for each framework are appropriate for training on each dataset without per-dataset tuning.
    Section 4.1: models trained from scratch with default hyperparameters (Table 4).

how reviews work

0 comments
Cite this review

Pith. "Pith review of RPEE-HEADS: A Novel Benchmark for Pedestrian Head Detection in Crowd Videos." pith.science (2026). https://pith.science/paper/264G6I65

@misc{pith2026241118164,
  author       = {Pith},
  title        = {Pith review of: RPEE-HEADS: A Novel Benchmark for Pedestrian Head Detection in Crowd Videos},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/264G6I65}},
  note         = {Machine review of arXiv:2411.18164}
}
read the original abstract

The automatic detection of pedestrian heads in crowded environments is essential for crowd analysis and management tasks, particularly in high-risk settings such as railway platforms and event entrances. These environments, characterized by dense crowds and dynamic movements, are underrepresented in public datasets, posing challenges for existing deep learning models. To address this gap, we introduce the Railway Platforms and Event Entrances-Heads (RPEE-Heads) dataset, a novel, diverse, high-resolution, and accurately annotated resource. It includes 109,913 annotated pedestrian heads across 1,886 images from 66 video recordings, with an average of 56.2 heads per image. Annotations include bounding boxes for visible head regions. In addition to introducing the RPEE-Heads dataset, this paper evaluates eight state-of-the-art object detection algorithms using the RPEE-Heads dataset and analyzes the impact of head size on detection accuracy. The experimental results show that You Only Look Once v9 and Real-Time Detection Transformer outperform the other algorithms, achieving mean average precisions of 90.7% and 90.8%, with inference times of 11 and 14 milliseconds, respectively. Moreover, the findings underscore the need for specialized datasets like RPEE-Heads for training and evaluating accurate models for head detection in railway platforms and event entrances. The dataset and pretrained models are available at https://doi.org/10.34735/ped.2024.2.

Figures

Figures reproduced from arXiv: 2411.18164 by the authors.

Figure 1
Figure 1. Illustrative examples from the selected data sources. [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. a) A visual example of manual annotation, with red boxes and green corners indicating head annotations. b) [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. The distribution of the number of annotated heads among the selected images. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Distribution of the sizes of bounding boxes across the training, validation, and test sets. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Performance evaluation using precision-recall [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 7
Figure 7. Figure 7: Visual comparison of models trained on the introduced dataset (real-world experiment scenario). [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: Precision-recall curves of diverse models and public datasets in the context of railway platforms and event [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

62 extracted references · 49 canonical work pages

  1. [1]

    Computer vision and deep learning techniques for pedestrian detection and tracking: A survey

    Antonio Brunetti, Domenico Buongiorno, Gianpaolo Francesco Trotta, and Vitoantonio Bevilacqua. Computer vision and deep learning techniques for pedestrian detection and tracking: A survey. Neurocomputing, 300:17–33, 2018

    work page 2018

  2. [2]

    Deep learning in crowd counting: A survey

    Lijia Deng, Qinghua Zhou, Shuihua Wang, Juan Manuel Górriz, and Yudong Zhang. Deep learning in crowd counting: A survey. CAAI Transactions on Intelligence Technology, 2023

    work page 2023

  3. [3]

    Trajectory-based motion pattern analysis of crowds.Neuro- computing, 247:213–223, 2017

    Wei Lu, Xiang Wei, Weiwei Xing, and Weibin Liu. Trajectory-based motion pattern analysis of crowds.Neuro- computing, 247:213–223, 2017

    work page 2017

  4. [4]

    A survey of crowd counting and density estimation based on convolutional neural network

    Zizhu Fan, Hong Zhang, Zheng Zhang, Guangming Lu, Yudong Zhang, and Yaowei Wang. A survey of crowd counting and density estimation based on convolutional neural network. Neurocomputing, 472:224–251, 2022

    work page 2022

  5. [5]

    A novel voronoi-based convolutional neural network framework for pushing person detection in crowd videos

    Ahmed Alia, Mohammed Maree, Mohcine Chraibi, and Armin Seyfried. A novel voronoi-based convolutional neural network framework for pushing person detection in crowd videos. Complex & Intelligent Systems, pages 1–27, 2024

    work page 2024

  6. [6]

    The impact of yolo algorithms within fall detection application: A review

    Ahlam R Khekan, Hadi S Aghdasi, and Pedram Salehpour. The impact of yolo algorithms within fall detection application: A review. IEEE Access, 2024

    work page 2024

  7. [7]

    A cloud-based deep learning framework for early detection of pushing at crowded event entrances

    Ahmed Alia, Mohammed Maree, Mohcine Chraibi, Anas Toma, and Armin Seyfried. A cloud-based deep learning framework for early detection of pushing at crowded event entrances. IEEE access, 11:45936–45949, 2023

    work page 2023

  8. [8]

    A hybrid deep learning and visualization framework for pushing behavior detection in pedestrian dynamics

    Ahmed Alia, Mohammed Maree, and Mohcine Chraibi. A hybrid deep learning and visualization framework for pushing behavior detection in pedestrian dynamics. Sensors, 22(11):4040, 2022

    work page 2022

Show all 62 references
  1. [9]

    On the exploitation of gps-based data for real-time visualisation of pedestrian dynamics in open environments

    Ahmed Alia, Mohammed Maree, and Mohcine Chraibi. On the exploitation of gps-based data for real-time visualisation of pedestrian dynamics in open environments. Behaviour & Information Technology, 41(8):1709– 1723, 2022

    2022

  2. [10]

    Subway diaries: How people experience and practice riding the train

    Richard E Ocejo and Stéphane Tonnelat. Subway diaries: How people experience and practice riding the train. Ethnography, 15(4):493–515, 2014

    2014

  3. [11]

    A fast hybrid deep neural network model for pushing behavior detection in human crowds

    Ahmed Alia, Mohammed Maree, and Mohcine Chraibi. A fast hybrid deep neural network model for pushing behavior detection in human crowds. In 2022 IEEE/ACS 19th International Conference on Computer Systems and Applications (AICCSA), pages 1–2. IEEE, 2022

    2022

  4. [12]

    Unihead: unifying multi-perception for detection heads

    Hantao Zhou, Rui Yang, Yachao Zhang, Haoran Duan, Yawen Huang, Runze Hu, Xiu Li, and Yefeng Zheng. Unihead: unifying multi-perception for detection heads. IEEE Transactions on Neural Networks and Learning Systems, 2024

    2024

  5. [13]

    Semantic head enhanced pedestrian detection in a crowd

    Ruiqi Lu, Huimin Ma, and Yu Wang. Semantic head enhanced pedestrian detection in a crowd. Neurocomputing, 400:343–351, 2020

    2020

  6. [14]

    Scale and density invariant head detection deep model for crowd counting in pedestrian crowds

    Sultan Daud Khan and Saleh Basalamah. Scale and density invariant head detection deep model for crowd counting in pedestrian crowds. The Visual Computer, 37(8):2127–2137, 2021

    2021

  7. [15]

    Convolutional neural network: a review of models, methodologies and applications to object detection

    Anamika Dhillon and Gyanendra K Verma. Convolutional neural network: a review of models, methodologies and applications to object detection. Progress in Artificial Intelligence, 9(2):85–112, 2020

    2020

  8. [16]

    Enhanced binary cuckoo search with frequent values and rough set theory for feature selection

    Ahmed Alia and Adel Taweel. Enhanced binary cuckoo search with frequent values and rough set theory for feature selection. IEEE access, 9:119430–119453, 2021

    2021

  9. [17]

    Convolutional neural networks-an extensive arena of deep learning

    Navdeep Singh and Hiteshwari Sabrol. Convolutional neural networks-an extensive arena of deep learning. a comprehensive study. Archives of Computational Methods in Engineering, 28(7):4755–4780, 2021

    2021

  10. [18]

    YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

    Chien-Yao Wang, Alexey Bochkovskiy, and Hong-Yuan Mark Liao. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023

    2023

  11. [19]

    Ultralytics. Yolov8. https://github.com/ultralytics/ultralytics, 2023. Available Online, Accessed on December 2023

    2023

  12. [20]

    YOLOv9: Learning what you want to learn using programmable gradient information

    Chien-Yao Wang and Hong-Yuan Mark Liao. YOLOv9: Learning what you want to learn using programmable gradient information. 2024

    2024

  13. [21]

    Rich feature hierarchies for accurate object detection and semantic segmentation

    Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 580–587, 2014. 15 RPEE-HEADS

    2014

  14. [22]

    Fast r-cnn

    Ross Girshick. Fast r-cnn. In Proceedings of the IEEE international conference on computer vision , pages 1440–1448, 2015

    2015

  15. [23]

    Faster r-cnn: Towards real-time object detection with region proposal networks

    Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015

    2015

  16. [24]

    Cascade r-cnn: Delving into high quality object detection

    Zhaowei Cai and Nuno Vasconcelos. Cascade r-cnn: Delving into high quality object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 6154–6162, 2018

    2018

  17. [25]

    Detecting heads using feature refine net and cascaded multi-scale architecture

    Dezhi Peng, Zikai Sun, Zirong Chen, Zirui Cai, Lele Xie, and Lianwen Jin. Detecting heads using feature refine net and cascaded multi-scale architecture. arXiv preprint arXiv:1803.09256, 2018

    2018 arXiv

  18. [26]

    Context-aware CNNs for person head detection

    Tuan-Hung Vu, Anton Osokin, and Ivan Laptev. Context-aware CNNs for person head detection. In International Conference on Computer Vision (ICCV), 2015

    2015

  19. [27]

    Nwpu-crowd: A large-scale benchmark for crowd counting and localization

    Qi Wang, Junyu Gao, Wei Lin, and Xuelong Li. Nwpu-crowd: A large-scale benchmark for crowd counting and localization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 2021

    2021

  20. [28]

    Sindagi, Rajeev Yasarla, and Vishal M

    Vishwanath A. Sindagi, Rajeev Yasarla, and Vishal M. Patel. Jhu-crowd++: Large-scale crowd counting dataset and a benchmark method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 2022

    2022

  21. [29]

    Maritime small object detection algorithm in drone aerial images based on improved yolov8

    Ling Peng, Yihong Zhang, and Shuai Ma. Maritime small object detection algorithm in drone aerial images based on improved yolov8. IEEE Access, 2024

    2024

  22. [30]

    Feature mining for localised crowd counting

    Ke Chen, Chen Change Loy, Shaogang Gong, and Tony Xiang. Feature mining for localised crowd counting. In Bmvc, volume 1, page 3, 2012

    2012

  23. [31]

    Crowd counting via scale-adaptive convolutional neural network

    Lu Zhang, Miaojing Shi, and Qiaobo Chen. Crowd counting via scale-adaptive convolutional neural network. In 2018 IEEE winter conference on applications of computer vision (WACV), pages 1113–1121. IEEE, 2018

    2018

  24. [32]

    Counting people based on linear, weighted, and local random forests

    Helia Farhood, Xiangjian He, Wenjing Jia, Michael Blumenstein, and Hanhui Li. Counting people based on linear, weighted, and local random forests. In 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pages 1–7. IEEE, 2017

    2017

  25. [33]

    Focal loss for dense object detection

    Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision, pages 2980–2988, 2017

    2017

  26. [34]

    Detrs beat yolos on real-time object detection

    Yian Zhao, Wenyu Lv, Shangliang Xu, Jinman Wei, Guanzhong Wang, Qingqing Dang, Yi Liu, and Jie Chen. Detrs beat yolos on real-time object detection. arXiv preprint arXiv:2304.08069, 2023

    2023 arXiv

  27. [35]

    Context-aware cnns for person head detection

    Tuan-Hung Vu, Anton Osokin, and Ivan Laptev. Context-aware cnns for person head detection. In Proceedings of the IEEE International Conference on Computer Vision, pages 2893–2901, 2015

    2015

  28. [36]

    Localized region context and object feature fusion for people head detection

    Yule Li, Yong Dou, Xinwang Liu, and Teng Li. Localized region context and object feature fusion for people head detection. In 2016 IEEE International Conference on Image Processing (ICIP), pages 594–598. IEEE, 2016

    2016

  29. [37]

    Headnet: pedestrian head detection utilizing body in context

    Gang Chen, Xufen Cai, Hu Han, Shiguang Shan, and Xilin Chen. Headnet: pedestrian head detection utilizing body in context. In 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pages 556–563. IEEE, 2018

    2018

  30. [38]

    Robust person head detection based on multi-scale representation fusion of deep convolution neural network

    Yingying Wang, Yingjie Yin, Wenqi Wu, Siyang Sun, and Xingang Wang. Robust person head detection based on multi-scale representation fusion of deep convolution neural network. In 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO), pages 296–301. IEEE, 2017

    2017

  31. [39]

    Disam: Density independent and scale aware model for crowd counting and localization

    Sultan Daud Khan, Habib Ullah, Mohammad Uzair, Mohib Ullah, Rehan Ullah, and Faouzi Alaya Cheikh. Disam: Density independent and scale aware model for crowd counting and localization. In 2019 IEEE International Conference on Image Processing (ICIP), pages 4474–4478. IEEE, 2019

    2019

  32. [40]

    Crowd counting using deep learning based head detection

    Maryam Hassan, Farhan Hussain, Sultan Daud Khan, Mohib Ullah, Mudassar Yamin, and Habib Ullah. Crowd counting using deep learning based head detection. Electronic Imaging, 35:293–1, 2023

    2023

  33. [41]

    Pedestrian head detection and tracking via global vision transformer

    Xuan-Thuy V o, Van-Dung Hoang, Duy-Linh Nguyen, and Kang-Hyun Jo. Pedestrian head detection and tracking via global vision transformer. In International Workshop on Frontiers of Computer Vision , pages 155–167. Springer, 2022

    2022

  34. [42]

    Locality-constrained spatial transformer network for video crowd counting

    Yanyan Fang, Biyun Zhan, Wandi Cai, Shenghua Gao, and Bo Hu. Locality-constrained spatial transformer network for video crowd counting. volume 2019-July, 2019

    2019

  35. [43]

    Tracking pedestrian heads in dense crowd

    Ramana Sundararaman, Cedric De Almeida Braga, Eric Marchand, and Julien Pettre. Tracking pedestrian heads in dense crowd. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3865–3875, 2021

    2021

  36. [44]

    Crowdhuman: A benchmark for detecting human in a crowd

    Shuai Shao, Zijian Zhao, Boxun Li, Tete Xiao, Gang Yu, Xiangyu Zhang, and Jian Sun. Crowdhuman: A benchmark for detecting human in a crowd. arXiv preprint arXiv:1805.00123, 2018. 16 RPEE-HEADS

    2018 arXiv

  37. [45]

    How far are we from solving pedestrian detection? In Proceedings of the iEEE conference on computer vision and pattern recognition, pages 1259–1267, 2016

    Shanshan Zhang, Rodrigo Benenson, Mohamed Omran, Jan Hosang, and Bernt Schiele. How far are we from solving pedestrian detection? In Proceedings of the iEEE conference on computer vision and pattern recognition, pages 1259–1267, 2016

    2016

  38. [46]

    Crowd management in transport infrastructures (project number 13n14530 to 13n14533)

    CroMa Project. Crowd management in transport infrastructures (project number 13n14530 to 13n14533). https://www.croma-projekt.de/de, 2018

    2018

  39. [47]

    https://doi.org/10.34735/ped.da, 2018

    Data archive of experimental data from studies about pedestrian dynamics. https://doi.org/10.34735/ped.da, 2018

    2018 doi

  40. [48]

    Collective phenomena in crowds—where pedestrian dynamics need social psychology

    Anna Sieben, Jette Schumann, and Armin Seyfried. Collective phenomena in crowds—where pedestrian dynamics need social psychology. PLoS one, 12(6):e0177328, 2017

    2017

  41. [49]

    Crowds in front of bottlenecks at entrances from the perspective of physics and social psychology

    Juliane Adrian, Armin Seyfried, and Anna Sieben. Crowds in front of bottlenecks at entrances from the perspective of physics and social psychology. Journal of the Royal Society Interface, 17(165):20190871, 2020

    2020

  42. [50]

    Influence of corridor width and motivation on pedestrians in front of bottlenecks

    Juliane Adrian, Maik Boltes, Anna Sieben, and Armin Seyfried. Influence of corridor width and motivation on pedestrians in front of bottlenecks. In Traffic and Granular Flow 2019, pages 3–9. Springer, 2020

    2019

  43. [51]

    Redefining the role of obstacles in pedestrian evacuation

    Angel Garcimartín, Diego Maza, José Martín Pastor, Daniel Ricardo Parisi, César Martín-Gómez, and Iker Zuriguel. Redefining the role of obstacles in pedestrian evacuation. New Journal of Physics, 20(12):123025, 2018

    2018

  44. [52]

    LabelImg

    Tzutalin. LabelImg. https://github.com/HumanSignal/labelImg, 2015

    2015

  45. [53]

    Optimal training and test sets design for machine learning

    Burkay Genc and HÜSEY˙IN Tunc. Optimal training and test sets design for machine learning. Turkish Journal of Electrical Engineering & Computer Sciences, 27(2):1534–1545, 2019

    2019

  46. [54]

    Ultralytics YOLO, January 2023

    Glenn Jocher, Ayush Chaurasia, and Jing Qiu. Ultralytics YOLO, January 2023

    2023

  47. [55]

    Detectron2.https://github

    Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick. Detectron2.https://github. com/facebookresearch/detectron2, 2019

    2019

  48. [56]

    Pedregosa, G

    F. Pedregosa, G. Varoquaux, A. Gramfort, V . Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V . Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learni...

    2011

  49. [57]

    Pytorch: An imperative style, high-performance deep learning library

    Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu ...

    2019

  50. [58]

    G. Bradski. The OpenCV Library. Dr. Dobb’s Journal of Software Tools, 2000

    2000

  51. [59]

    Aphid cluster recognition and detection in the wild using deep learning models

    Tianxiao Zhang, Kaidong Li, Xiangyu Chen, Cuncong Zhong, Bo Luo, Ivan Grijalva, Brian McCornack, Daniel Flippo, Ajay Sharda, and Guanghui Wang. Aphid cluster recognition and detection in the wild using deep learning models. Scientific Reports, 13(1):13410, 2023

    2023

  52. [60]

    The pascal visual object classes (voc) challenge

    Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88:303–338, 2010

    2010

  53. [61]

    I had a bad day: Challenges of object detection in bad visibility conditions

    Thomas Rothmeier, Diogo Wachtel, Tetmar von Dem Bussche-Hünnefeld, and Werner Huber. I had a bad day: Challenges of object detection in bad visibility conditions. In 2023 IEEE Intelligent Vehicles Symposium (IV), pages 1–6. IEEE, 2023

    2023

  54. [62]

    Object detection using yolo: Challenges, architectural successors, datasets and applications

    Tausif Diwan, G Anirudh, and Jitendra V Tembhurne. Object detection using yolo: Challenges, architectural successors, datasets and applications. multimedia Tools and Applications, 82(6):9243–9275, 2023. 17

    2023

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.