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REVIEW 4 major objections 6 minor 74 references

DAVE: Diverse Atomic Visual Elements Dataset with High Representation of Vulnerable Road Users in Complex and Unpredictable Environments

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read DAVE, a 13-million-box Indian road dataset, claims that current perception methods degrade sharply on dense unstructured traffic where vulnerable road users make up 41.13% of instances.

desk verdict DAVE is a potentially valuable dataset for dense Asian traffic, but the paper's main 'more challenging' evidence has a label-space flaw and the dataset isn't released or quality-checked. read the letter →

arxiv 2412.20042 v1 pith:3KKHXXGW submitted 2024-12-28 cs.CV

Xijun Wang , Pedro Sandoval-Segura , Chengyuan Zhang , Junyun Huang , Tianrui Guan , Ruiqi Xian , Fuxiao Liu , Rohan Chandra
show 2 more authors
Boqing Gong Dinesh Manocha
This is my paper · ORCID
classification cs.CV
keywords trafficvideodatasetvulnerableroadusersunstructuredenvironmentsdensebounding-boxannotationsactionrecognitionautonomousdrivingperceptiondomainshiftIndiascenes
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

DAVE is a new traffic-video dataset built from Indian road scenes, where traffic is denser, less structured, and more mixed than the Western highways in Waymo. The paper contends that state-of-the-art perception models trained on existing datasets degrade substantially when evaluated on DAVE, and that the dataset's 41.13% share of vulnerable road users makes it a more safety-relevant test than Waymo's 23.14%. If that is right, DAVE gives the field a way to measure how well detection, tracking, and video-understanding systems generalize to the kind of heterogeneous, rule-bending traffic that dominates much of the world. The practical stake is that perception which fails on these scenes is not ready for global deployment.

What carries the argument

The central object is DAVE itself, a manually annotated corpus of 1,231 one-minute dashcam videos from urban and semi-urban India in which every visible actor is treated as an atomic visual element. Its operational mechanism is the dual annotation: each of over 13 million bounding boxes gets an actor identity from 16 classes, and more than 1.6 million boxes additionally receive an action label from 16 classes such as cut-in, overtaking, zigzag movement, and U-turn. That combination lets one corpus feed five different video benchmarks, while the dense, unstructured scenes push models beyond the clean settings of Western traffic datasets.

What would settle it

Independently re-annotate a random sample of DAVE frames and measure inter-annotator agreement on bounding boxes and action labels; if agreement falls below standard thresholds (for example, mean IoU below 0.5 or action-label agreement below 80%), the ground truth and every reported benchmark number lose their foundation.

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Extended reading notes

Core claim

The paper's central claim is that DAVE, the Diverse Atomic Visual Elements dataset, is a large-scale, manually annotated traffic-video benchmark from urban and semi-urban India that is substantially harder and more representative of vulnerable road users than existing datasets such as Waymo. DAVE contains 1,231 one-minute dashcam videos with over 13 million bounding boxes, more than 1.6 million of which also carry one of 16 action labels; actors span 16 categories including animals, motorized tricycles, scooters, and pedestrians. The paper reports that vulnerable road users account for 41.13% of instances, versus 23.14% in Waymo. Across five video tasks, current methods score far lower on DAVE than on their original benchmarks: ARTrack's SR0.75 drops 23.7% relative to GOT-10k, Swin-T reaches 32.5 mAP versus 50.5 on COCO, ACAR-Net gets 6.3% mAP versus 33.3% on AVA v2.2, CG-DETR attains 5.1 R1@0.5 versus 58.4 on Charades-STA, and SlowFast gets 41.0 mAP versus 45.2 on Charades. The conclusion drawn is that DAVE exposes a real perception gap for dense, unpredictable, rule-bending traffic and offers a testbed for building models that protect vulnerable road users.

Load-bearing premise

The load-bearing premise is that the manual annotations are accurate and consistent enough to serve as ground truth for all five benchmark tasks, and that the reported statistics (13,012,635 boxes, 41.13% vulnerable-road-user share, action-label counts) were computed correctly.

Editorial extensions

If this is right

  • Training on DAVE's vulnerable-road-user instances instead of Waymo's lifts a YOLOv8 detector's mAP50 from 0.00266 to 0.235 on DAVE validation, and combining both datasets raises it further to 0.267.
  • ARTrack's success rate at 0.75 IoU drops 23.7% on DAVE relative to GOT-10k, indicating that current trackers lose precise localization in dense, cluttered scenes.
  • Methods on four other video tasks—Swin-T for detection, ACAR-Net for spatiotemporal action localization, CG-DETR for moment retrieval, and SlowFast for multi-label action recognition—all score far lower on DAVE than on their original benchmarks.
  • Adding DAVE to an existing dataset like Waymo produces better vulnerable-road-user detection than either dataset alone, pointing toward merged, more globally representative training corpora.

Reading between the lines

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

  • Editorial inference: if the annotations are released, DAVE could serve as a geographic domain-shift probe, quantifying how much each model's performance drops when moving from Western to Indian traffic.
  • Editorial inference: because the paper reports no inter-annotator agreement, an independent re-annotation of a random sample would be the first validating experiment a user should run before trusting the benchmark numbers.
  • Editorial inference: the action taxonomy covers vehicle maneuvers almost exclusively; extending it to pedestrian and animal behaviors would strengthen the safety argument implied by the high vulnerable-road-user share.
  • Editorial inference: the recorded GPS and camera intrinsics could support trajectory forecasting and monocular 3D localization, tasks not benchmarked in the paper.
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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

4 major / 6 minor

Summary. This paper introduces DAVE, a manually annotated dashcam video dataset collected in India, with 16 actor categories, 16 action types, over 13 million bounding boxes, and over 1.6 million boxes that also carry action labels. The paper reports that VRUs constitute 41.13% of instances, compares this with Waymo, and evaluates existing models across five video tasks (tracking, detection, video moment retrieval, spatiotemporal action localization, multi-label video action recognition), finding large performance drops on DAVE. The authors conclude that DAVE is more challenging and more representative of unstructured traffic than existing datasets.

Significance. If the annotation statistics and benchmark protocols are accurate, DAVE addresses a real gap: most traffic datasets are Western, structured, and underrepresent VRUs, while DAVE provides dense, action-labeled footage of heterogeneous Indian road scenes. The breadth of actor categories, the weather/density variety, and the coverage of five tasks make it a potentially useful benchmark. However, the contribution is currently not verifiable: the manuscript contains no data release, no annotation-quality metrics, and several inconsistencies in core numbers, and the key Waymo comparison has a label-space confound. These are fixable with revision, after which the dataset could be a solid resource for the community.

major comments (4)
  1. [Section 3.2.1, Table 5] The quantitative claim that DAVE is more challenging than Waymo rests on the YOLOv8 VRU detection comparison, but the paper never specifies how the Waymo label space (pedestrian, cyclist) is mapped to DAVE's VRU classes (Animal, Bicycle, MotorBike, MotorizedTricycle, MultiWheeler, Scooter, TriCycle). If the Waymo-trained model is evaluated against all DAVE VRU classes, the 0.00266 mAP50 is largely attributable to classes never seen during training rather than to scene difficulty. The authors should either evaluate on the intersection of class labels (e.g., pedestrian/cyclist subsets) or report the class-wise mapping and per-class results; otherwise the 'more challenging' conclusion is not supported.
  2. [Section 2.2] The dataset description relies on manual annotation with CVAT but reports no inter-annotator agreement, no quality-control protocol, and no spot-check statistics. Since every benchmark result in Sections 3.1-3.5 is computed against these annotations, the absence of any annotation-quality evidence leaves the ground-truth validity of all reported numbers unestablished. Provide IAA on a random sample (e.g., bbox IoU agreement and label agreement per class/action), and state how ambiguous cases (occlusion, small objects) were handled.
  3. [Abstract, Section 2.1, Section 3.5, Tables 3, 6, 8] Core statistics are internally inconsistent. The abstract reports 23.71% VRU share in Waymo while the Introduction reports 23.14%; Section 2.1 states 1920x1080 capture resolution while Table 6 lists 1920x1280; Section 3.4/Table 8 reports 1,600k action-annotated boxes while the Introduction says 1.6 million; Table 3 gives no total count of action instances; and Section 2.1 says the dataset contains 1231 clips while Section 3.5 says the multi-label split has 10,083 clips (8,166 + 1,917). These discrepancies need to be reconciled and the exact computation of the 41.13% VRU share and the 13,012,635 total boxes stated before the quantitative claims can be trusted.
  4. [Data availability] The manuscript provides no download link, hosting plan, or license for DAVE, even though the contribution is a dataset. Without access, no external researcher can verify the 13M/1.6M box statistics, the split sizes, or any benchmark result. Provide a clear availability statement (including any privacy restrictions, since faces and plates are said to be blurred) and a data-release mechanism.
minor comments (6)
  1. [Table 3 and Table 9] 'Breaking' should be 'Braking', and 'Charedes' in Table 9's caption should be 'Charades'.
  2. [Section 2.1] The dashcam 'resolution of 2.3 megapixels' does not match the stated video resolution of 1920x1080 (2.07 MP); clarify whether the sensor resolution and output resolution differ.
  3. [Table 2] 'NuScense' should be 'nuScenes'.
  4. [Section 3.1, Table 4] Clarify the relationship between the 44.8k frame sequences and the 5,227 filtered validation sequences, since Table 4 lists 'Sequence number' as 44.8k while the text reports only the validation filtering.
  5. [Figure 3] Add axis labels and legends to the actor/action distribution panels; as rendered in the text, the quantitative distributions cannot be read from the figure.
  6. [Abstract/Keywords] The keyword 'Big data-driven models' is vague and could be removed or replaced with more specific terms such as 'action recognition' and 'object tracking'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the dataset's statistics and benchmark degradation numbers are empirically measured, not derived from the paper's own assumptions or fitted parameters.

full rationale

DAVE is a dataset and benchmarking paper, so the derivation chain under review is not a theory-derived prediction. The central claims—over 13 million annotated boxes, 41.13% VRUs, and that existing models degrade on DAVE—are empirical measurements. The degradation numbers are produced by running externally released models (ARTrack-256, YOLOv8, Swin-T, CG-DETR, ACAR-Net, SlowFast) under stated training and evaluation protocols, so they do not reduce by construction to the dataset statistics. Several references cite the authors' prior work on dense heterogeneous traffic (e.g., [6], [9], [14], [35], [36]), but these are contextual and motivational; none is invoked as a uniqueness theorem or as the source of an ansatz that defines DAVE's annotations. The claim that DAVE is 'more challenging' than Waymo rests on Table 5, which may suffer from label-space mismatch between DAVE's eight VRU categories and Waymo's pedestrian/cyclist classes; if so, that is a validity or reproducibility concern, not circularity, because the performance gap is not logically forced by how the dataset was defined. No fitted parameter is renamed as a prediction, and no result is defined in terms of the conclusion it is used to support. Therefore no significant circularity.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The paper's central claims rest on the representativeness and accuracy of the collected data, not on a mathematical derivation. The main unpaid-for assumptions are that the dashcam footage is representative of complex Asian traffic and that manual annotations are reliable. No free parameters are introduced, and no new physical entities are postulated.

assumptions (2)
  • domain assumption The dashcam videos from two vehicles in India are representative of complex and unpredictable Asian traffic.
    The paper's central value claim depends on the collected data being representative of the target domain; no statistical sampling details are provided (Section 2.1).
  • domain assumption Manual annotations are accurate enough to serve as ground truth for benchmarking.
    Annotation quality is not validated; no inter-annotator agreement or error rate is reported (Section 2.2).

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Cite this review

Pith. "Pith review of DAVE: Diverse Atomic Visual Elements Dataset with High Representation of Vulnerable Road Users in Complex and Unpredictable Environments." pith.science (2026). https://pith.science/paper/3KKHXXGW

@misc{pith2026241220042,
  author       = {Pith},
  title        = {Pith review of: DAVE: Diverse Atomic Visual Elements Dataset with High Representation of Vulnerable Road Users in Complex and Unpredictable Environments},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3KKHXXGW}},
  note         = {Machine review of arXiv:2412.20042}
}
read the original abstract

Most existing traffic video datasets including Waymo are structured, focusing predominantly on Western traffic, which hinders global applicability. Specifically, most Asian scenarios are far more complex, involving numerous objects with distinct motions and behaviors. Addressing this gap, we present a new dataset, DAVE, designed for evaluating perception methods with high representation of Vulnerable Road Users (VRUs: e.g. pedestrians, animals, motorbikes, and bicycles) in complex and unpredictable environments. DAVE is a manually annotated dataset encompassing 16 diverse actor categories (spanning animals, humans, vehicles, etc.) and 16 action types (complex and rare cases like cut-ins, zigzag movement, U-turn, etc.), which require high reasoning ability. DAVE densely annotates over 13 million bounding boxes (bboxes) actors with identification, and more than 1.6 million boxes are annotated with both actor identification and action/behavior details. The videos within DAVE are collected based on a broad spectrum of factors, such as weather conditions, the time of day, road scenarios, and traffic density. DAVE can benchmark video tasks like Tracking, Detection, Spatiotemporal Action Localization, Language-Visual Moment retrieval, and Multi-label Video Action Recognition. Given the critical importance of accurately identifying VRUs to prevent accidents and ensure road safety, in DAVE, vulnerable road users constitute 41.13% of instances, compared to 23.71% in Waymo. DAVE provides an invaluable resource for the development of more sensitive and accurate visual perception algorithms in the complex real world. Our experiments show that existing methods suffer degradation in performance when evaluated on DAVE, highlighting its benefit for future video recognition research.

Figures

Figures reproduced from arXiv: 2412.20042 by the authors.

Figure 1
Figure 1. Tasks Overview. We use DAVE for various video recognition tasks, including Tracking, Detection, Video Moment Retrieval, Spatiotemporal Action Localization, and Multi-label Video Action Recognition. Our large-scale dataset is made up of complex environments that are densely annotated. Each bounding box (bbox) corresponds to an actor, and the text above each bbox serves as either the tracking ID or indicates the assoc… view at source ↗
Figure 2
Figure 2. Challenging Characteristics of DAVE: These videos correspond to different times of the day with different brightness, different geographical landforms from city and rural areas, high density and unpredictable road conditions, diverse actors including humans, animals, vehicles, etc. • Sparse Annotations: As shown in [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

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Works this paper leans on

74 extracted references · 65 canonical work pages

  1. [1]

    In: Proceedings of the IEEE/CVF international conference on computer vision

    Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., Gall, J.: Semantickitti: A dataset for semantic scene understanding of lidar sequences. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 9297–9307 (2019)

    work page 2019

  2. [2]

    CVPR (2020)

    Caesar, H., Bankiti, V ., Lang, A.H., V ora, S., Liong, V .E., Xu, Q., Krishnan, A., Pan, Y ., Baldan, G., Beijbom, O.: nuscenes: A multimodal dataset for autonomous driving. CVPR (2020)

    work page 2020

  3. [3]

    In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Cai, Z., Vasconcelos, N.: Cascade r-cnn: Delving into high quality object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 6154–6162 (2018)

    work page 2018

  4. [4]

    In: European Conference on Computer Vision (ECCV)

    Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: European Conference on Computer Vision (ECCV). pp. 213–229. Springer (2020)

    work page 2020

  5. [5]

    arXiv preprint arXiv:1907.06987 (2019)

    Carreira, J., Noland, E., Hillier, C., Zisserman, A.: A short note on the kinetics-700 human action dataset. arXiv preprint arXiv:1907.06987 (2019)

    arXiv 2019

  6. [6]

    Chandra, R.: Towards autonomous driving in dense, heterogeneous, and unstructured traffic. Ph.D. thesis, University of Maryland, College Park (2022)

    work page 2022

  7. [7]

    In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

    Chandra, R., Bhattacharya, U., Bera, A., Manocha, D.: Densepeds: Pedestrian tracking in dense crowds using front-rvo and sparse features. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 468–475. IEEE (2019)

    work page 2019

  8. [8]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Chandra, R., Bhattacharya, U., Bera, A., Manocha, D.: Traphic: Trajectory prediction in dense and heterogeneous traffic using weighted interactions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8483–8492 (2019)

    work page 2019

Show all 74 references
  1. [9]

    In: 2020 IEEE International Conference on Robotics and Automation (ICRA)

    Chandra, R., Bhattacharya, U., Randhavane, T., Bera, A., Manocha, D.: Roadtrack: Realtime tracking of road agents in dense and heterogeneous environments. In: 2020 IEEE International Conference on Robotics and Automation (ICRA). pp. 1270–1277. IEEE (2020)

    2020

  2. [10]

    In: Proceedings of the 3rd ACM Computer Science in Cars Symposium

    Chandra, R., Bhattacharya, U., Roncal, C., Bera, A., Manocha, D.: Robusttp: End-to-end trajectory prediction for heterogeneous road-agents in dense traffic with noisy sensor inputs. In: Proceedings of the 3rd ACM Computer Science in Cars Symposium. pp. 1–9 (2019)

    2019

  3. [11]

    IEEE Robotics and Automation Letters 5(3), 4882–4890 (2020)

    Chandra, R., Guan, T., Panuganti, S., Mittal, T., Bhattacharya, U., Bera, A., Manocha, D.: Forecast- ing trajectory and behavior of road-agents using spectral clustering in graph-lstms. IEEE Robotics and Automation Letters 5(3), 4882–4890 (2020)

    2020

  4. [12]

    IEEE Robotics and Automation Letters 7(2), 2676–2683 (2022)

    Chandra, R., Manocha, D.: Gameplan: Game-theoretic multi-agent planning with human drivers at intersections, roundabouts, and merging. IEEE Robotics and Automation Letters 7(2), 2676–2683 (2022)

    2022

  5. [13]

    In: 2022 International Conference on Robotics and Automation (ICRA)

    Chandra, R., Wang, M., Schwager, M., Manocha, D.: Game-theoretic planning for autonomous driving among risk-aware human drivers. In: 2022 International Conference on Robotics and Automation (ICRA). pp. 2876–2883 (2022)

    2022

  6. [14]

    In: 2023 IEEE International Conference on Robotics and Automation (ICRA)

    Chandra, R., Wang, X., Mahajan, M., Kala, R., Palugulla, R., Naidu, C., Jain, A., Manocha, D.: Meteor: A dense, heterogeneous, and unstructured traffic dataset with rare behaviors. In: 2023 IEEE International Conference on Robotics and Automation (ICRA). pp. 9169–9175. IEEE (2023)

    2023

  7. [15]

    In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Chang, M.F., Lambert, J., Sangkloy, P., Singh, J., Bak, S., Hartnett, A., Wang, D., Carr, P., Lucey, S., Ramanan, D., et al.: Argoverse: 3d tracking and forecasting with rich maps. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 8748–...

    2019

  8. [16]

    In: Proceedings of the IEEE conference on computer vision and pattern recognition

    Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3213–3223 (2016)

    2016

  9. [17]

    In: Proceedings of the IEEE/CVF winter conference on applications of computer vision

    Corona, K., Osterdahl, K., Collins, R., Hoogs, A.: Meva: A large-scale multiview, multimodal video dataset for activity detection. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision. pp. 1060–1068 (2021)

    2021

  10. [18]

    CV AT.ai Corporation: Computer Vision Annotation Tool (CV AT) (Nov 2023),https://github.com/ opencv/cvat

    2023

  11. [19]

    Dave, A., Khurana, T., Tokmakov, P., Schmid, C., Ramanan, D.: Tao: A large-scale benchmark for tracking any object (2020)

    2020

  12. [20]

    In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Deng, J., Guo, J., Ververas, E., Kotsia, I., Zafeiriou, S.: Retinaface: Single-shot multi-level face localisation in the wild. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 5203–5212 (2020)

    2020

  13. [21]

    Ding, L., Terwilliger, J., Sherony, R., Reimer, B., Fridman, L.: Mit driveseg (manual) dataset for dynamic driving scene segmentation. Tech. Rep., Technical report, Massachusetts Institute of Technology (2020)

    2020

  14. [22]

    International journal of computer vision 88(2), 303–338 (2010)

    Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. International journal of computer vision 88(2), 303–338 (2010)

    2010

  15. [23]

    International Journal of Computer Vision 129, 439–461 (2021)

    Fan, H., Bai, H., Lin, L., Yang, F., Chu, P., Deng, G., Yu, S., Harshit, Huang, M., Liu, J., et al.: Lasot: A high-quality large-scale single object tracking benchmark. International Journal of Computer Vision 129, 439–461 (2021)

    2021

  16. [24]

    In: IEEE Interna- tional Conference on Computer Vision (ICCV)

    Feichtenhofer, C., Fan, H., Malik, J., He, K.: Slowfast networks for video recognition. In: IEEE Interna- tional Conference on Computer Vision (ICCV). pp. 6202–6211 (2019)

    2019

  17. [25]

    Geyer, J., Kassahun, Y ., Mahmudi, M., Ricou, X., Durgesh, R., Chung, A.S., Hauswald, L., Pham, V .H., Mühlegg, M., Dorn, S., Fernandez, T., Jänicke, M., Mirashi, S., Savani, C., Sturm, M., V orobiov, O., Oelker, M., Garreis, S., Schuberth, P.: A2d2: Audi autonomous driving da...

    2020 arXiv

  18. [26]

    In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

    Gu, C., Sun, C., Ross, D.A., V ondrick, C., Pantofaru, C., Li, Y ., Vijayanarasimhan, S., Toderici, G., Ricco, S., Sukthankar, R., et al.: Ava: A video dataset of spatio-temporally localized atomic visual actions. In: Proceedings of the IEEE Conference on Computer Vision and P...

    2018

  19. [27]

    In: Proceedings of the IEEE/CVF winter conference on applications of computer vision

    Guan, T., Wang, J., Lan, S., Chandra, R., Wu, Z., Davis, L., Manocha, D.: M3detr: Multi-representation, multi-scale, mutual-relation 3d object detection with transformers. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision. pp. 772–782 (2022)

    2022

  20. [28]

    In: Proceedings of the IEEE International Conference on Computer Vision

    Hendricks, L.A., Wang, O., Shechtman, E., Sivic, J., Darrell, T., Russell, B.: Localizing moments in video with natural language. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 5803–5812 (2017)

    2017

  21. [29]

    IEEE transactions on pattern analysis and machine intelligence 43(5), 1562–1577 (2019)

    Huang, L., Zhao, X., Huang, K.: Got-10k: A large high-diversity benchmark for generic object tracking in the wild. IEEE transactions on pattern analysis and machine intelligence 43(5), 1562–1577 (2019)

    2019

  22. [30]

    IEEE Trans- actions on Pattern Analysis and Machine Intelligence 42(10), 2702 (2020)

    HuangXinyu, W., et al.: Theapolloscape opendatasetforautonomousdrivinganditsapplication. IEEE Trans- actions on Pattern Analysis and Machine Intelligence 42(10), 2702 (2020)

    2020

  23. [31]

    in the wild

    Idrees, H., Zamir, A.R., Jiang, Y .G., Gorban, A., Laptev, I., Sukthankar, R., Shah, M.: The thumos challenge on action recognition for videos “in the wild”. Computer Vision and Image Understanding 155, 1–23 (2017)

    2017

  24. [32]

    In: Proceedings of the IEEE International Conference on Computer Vision

    Jhuang, H., Gall, J., Zuffi, S., Schmid, C., Black, M.J.: Towards understanding action recognition. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 3192–3199 (2013)

    2013

  25. [33]

    Jocher, G., Chaurasia, A., Qiu, J.: Ultralytics yolov8 (2023), https://github.com/ultralytics/ ultralytics

    2023

  26. [34]

    arXiv preprint arXiv:1705.06950 (2017)

    Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al.: The kinetics human action video dataset. arXiv preprint arXiv:1705.06950 (2017)

    2017 arXiv

  27. [35]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision

    Kothandaraman, D., Chandra, R., Manocha, D.: Bomudanet: unsupervised adaptation for visual scene understanding in unstructured driving environments. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3966–3975 (2021) 15

    2021

  28. [36]

    In: Proceedings of the IEEE/CVF international conference on computer vision

    Kothandaraman, D., Chandra, R., Manocha, D.: Ss-sfda: Self-supervised source-free domain adaptation for road segmentation in hazardous environments. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 3049–3059 (2021)

    2021

  29. [37]

    In: Proceedings of the IEEE International Conference on Computer Vision (2017)

    Krishna, R., Hata, K., Ren, F., Fei-Fei, L., Carlos Niebles, J.: Dense-captioning events in videos. In: Proceedings of the IEEE International Conference on Computer Vision (2017)

    2017

  30. [38]

    In: Proceedings of the IEEE international conference on computer vision workshops

    Kristan, M., Matas, J., Leonardis, A., Felsberg, M., Cehovin, L., Fernandez, G., V ojir, T., Hager, G., Nebehay, G., Pflugfelder, R.: The visual object tracking vot2015 challenge results. In: Proceedings of the IEEE international conference on computer vision workshops. pp. 1–...

    2015

  31. [39]

    In: European Conference on Computer Vision

    Lei, J., Li, L., Zhou, L., Gan, Z., Berg, T.L., Bansal, M., Luo, J.: Tvr: A large-scale dataset for video-subtitle moment retrieval. In: European Conference on Computer Vision. Springer (2020)

    2020

  32. [40]

    arXiv preprint arXiv:2103.07514 (2021)

    Li, Y ., Xu, J., Qiu, Z., Tian, Y ., Hu, T., Wang, J., Li, J., Xiong, H., Hauptmann, A.G.: Multisports: A multi-person video dataset for action spotting, localization, and detection. arXiv preprint arXiv:2103.07514 (2021)

    2021 arXiv

  33. [41]

    IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3292–3310 (2022)

    Liao, Y ., Xie, J., Geiger, A.: Kitti-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(3), 3292–3310 (2022)

    2022

  34. [42]

    In: European conference on computer vision

    Lin, T.Y ., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European conference on computer vision. pp. 740–755. Springer (2014)

    2014

  35. [43]

    European conference on computer vision (2014)

    Lin, T.Y ., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. European conference on computer vision (2014)

    2014

  36. [44]

    arXiv preprint arXiv:2302.07919 (2023)

    Liu, F., Yacoob, Y ., Shrivastava, A.: Covid-vts: Fact extraction and verification on short video platforms. arXiv preprint arXiv:2302.07919 (2023)

    2023 arXiv

  37. [45]

    In: IEEE International Conference on Computer Vision (ICCV)

    Liu, Z., Lin, Y ., Cao, Y ., Hu, H., Wei, Y ., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: IEEE International Conference on Computer Vision (ICCV). pp. 10012–10022 (2021)

    2021

  38. [46]

    arXiv preprint arXiv:2402.1413 (2024)

    Liu, Z., Yang, Z., Xu, X., Jiang, C., Li, X., He, X.: Gdtm: An indoor geospatial tracking dataset with distributed multimodal sensors. arXiv preprint arXiv:2402.1413 (2024)

    2024

  39. [47]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Malla, S., Dariush, B., Choi, C.: Titan: Future forecast using action priors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11186–11196 (2020)

    2020

  40. [48]

    arXiv preprint arXiv:1603.00831 (2016)

    Milan, A., Leal-Taixé, L., Reid, I., Roth, S., Schindler, K.: Mot17: A benchmark for multi-object tracking. arXiv preprint arXiv:1603.00831 (2016)

    2016 arXiv

  41. [49]

    Moon, W., Hyun, S., Lee, S., Heo, J.P.: Correlation-guided query-dependency calibration in video representation learning for temporal grounding (2023)

    2023

  42. [50]

    In: Proceedings of the European conference on computer vision (ECCV)

    Muller, M., Bibi, A., Giancola, S., Alsubaihi, S., Ghanem, B.: Trackingnet: A large-scale dataset and benchmark for object tracking in the wild. In: Proceedings of the European conference on computer vision (ECCV). pp. 300–317 (2018)

    2018

  43. [51]

    In: CVPR

    Oh, S., Hoogs, A., Perera, A., Cuntoor, N., Chen, C.C., Lee, J.T., Mukherjee, S., Aggarwal, J., Lee, H., Davis, L., et al.: A large-scale benchmark dataset for event recognition in surveillance video. In: CVPR

  44. [52]

    In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Pan, J., Chen, S., Shou, M.Z., Liu, Y ., Shao, J., Li, H.: Actor-context-actor relation network for spatio- temporal action localization. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 464–474 (2021)

    2021

  45. [53]

    arXiv preprint arXiv:2405.05354 (2024)

    Parikh, C., Mishra, R.S., Chandra, R., Sarvadevabhatla, R.K.: Transfer-lmr: Heavy-tail driving behavior recognition in diverse traffic scenarios. arXiv preprint arXiv:2405.05354 (2024)

    2024 arXiv

  46. [54]

    In: 2019 International Conference on Robotics and Automation (ICRA)

    Patil, A., Malla, S., Gang, H., Chen, Y .T.: The h3d dataset for full-surround 3d multi-object detection and tracking in crowded urban scenes. In: 2019 International Conference on Robotics and Automation (ICRA). pp. 9552–9557. IEEE (2019)

    2019

  47. [55]

    In: The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2024), https://openreview.net/forum?id=5L05sLRIlQ 16

    Peng, L., Gao, J., Liu, X., Li, W., Dong, S., Zhang, Z., Fan, H., Zhang, L.: Vasttrack: Vast category visual object tracking. In: The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2024), https://openreview.net/forum?id=5L05sLRIlQ 16

    2024

  48. [56]

    In: 2020 IEEE International conference on Robotics and Automation (ICRA)

    Pham, Q.H., Sevestre, P., Pahwa, R.S., Zhan, H., Pang, C.H., Chen, Y ., Mustafa, A., Chandrasekhar, V ., Lin, J.: A* 3d dataset: Towards autonomous driving in challenging environments. In: 2020 IEEE International conference on Robotics and Automation (ICRA). pp. 2267–2273. IEEE (2020)

    2020

  49. [57]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision

    Rasouli, A., Kotseruba, I., Kunic, T., Tsotsos, J.K.: Pie: A large-scale dataset and models for pedestrian intention estimation and trajectory prediction. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 6262–6271 (2019)

    2019

  50. [58]

    Transactions of the Association for Computational Linguistics 1, 25–36 (2013)

    Regneri, M., Rohrbach, M., Wetzel, D., Thater, S., Schiele, B., Pinkal, M.: Grounding action descriptions in videos. Transactions of the Association for Computational Linguistics 1, 25–36 (2013)

    2013

  51. [59]

    In: Proceedings of the IEEE conference on computer vision and pattern recognition

    Ros, G., Sellart, L., Materzynska, J., Vazquez, D., Lopez, A.M.: The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3234–3243 (2016)

    2016

  52. [60]

    European Conference on Computer Vision (2016)

    Sigurdsson, G.A., Varol, G.V ., Wang, X., Farhadi, A., Laptev, I., Gupta, A.: Hollywood in homes: Crowdsourcing data collection for activity understanding. European Conference on Computer Vision (2016)

    2016

  53. [61]

    In: European Conference on Computer Vision

    Sigurdsson, G.A., Varol, G.V ., Wang, X., Farhadi, A., Laptev, I., Gupta, A.: Hollywood in homes: Crowdsourcing data collection for activity understanding. In: European Conference on Computer Vision. pp. 510–526. Springer (2016)

    2016

  54. [62]

    IEEE transactions on pattern analysis and machine intelligence 45(1), 1036–1054 (2022)

    Singh, G., Akrigg, S., Di Maio, M., Fontana, V ., Alitappeh, R.J., Khan, S., Saha, S., Jeddisaravi, K., Yousefi, F., Culley, J., et al.: Road: The road event awareness dataset for autonomous driving. IEEE transactions on pattern analysis and machine intelligence 45(1), 1036–10...

    2022

  55. [63]

    arXiv preprint arXiv:1212.0402 (2012)

    Soomro, K., Zamir, A.R., Shah, M.: Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402 (2012)

    2012 arXiv

  56. [64]

    CVPR (2020)

    Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V ., Tsui, P., Guo, J., Zhou, Y ., Chai, Y ., Caine, B., et al.: Scalability in perception for autonomous driving: Waymo open dataset. CVPR (2020)

    2020

  57. [65]

    Sun, S., Akhtar, N., Song, H., Mian, A., Shah, M.: Deep affinity network for multiple object tracking (2019)

    2019

  58. [66]

    Wang, J., Jiang, W., Ma, L., Liu, W., Xu, Y .: Bidirectional attentive fusion with context gating for dense video captioning (2018)

    2018

  59. [67]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Wei, X., Bai, Y ., Zheng, Y ., Shi, D., Gong, Y .: Autoregressive visual tracking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9697–9706 (2023)

    2023

  60. [68]

    In: IEEE transactions on pattern analysis and machine intelligence

    Wu, Y ., Lim, J., Yang, M.H.: Object tracking benchmark. In: IEEE transactions on pattern analysis and machine intelligence. vol. 37, pp. 1834–1848 (2013)

    2013

  61. [69]

    In: https://github.com/szad670401/HyperLPR (2023)

    Yan, j., Yu, J., Xiaoxiao: Hyperlpr3 - high performance license plate recognition framework. In: https://github.com/szad670401/HyperLPR (2023)

    2023

  62. [70]

    arXiv preprint arXiv:2004.03044 (2020)

    Yao, Y ., Wang, X., Xu, M., Pu, Z., Atkins, E., Crandall, D.: When, where, and what? a new dataset for anomaly detection in driving videos. arXiv preprint arXiv:2004.03044 (2020)

    2020 arXiv

  63. [71]

    In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Zhan, X., Wu, Q., Wang, M., Sun, J., He, T.: Lasot: A large-scale high-resolution benchmark for visual object tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4641–4650 (2019)

    2019

  64. [72]

    In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Zhang, X., Yu, F., He, B., Yang, M., Li, X., Zhu, J., Wang, J.: Posetrack: A dataset for person search, multi-object tracking and multi-person pose tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 14077–14086 (2021)

    2021

  65. [73]

    In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Zheng, Z., Tang, S., Guo, Y ., Liu, Y ., Zhou, S., Li, H.: Pointodyssey: A large-scale synthetic dataset for long-term point tracking. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 5216–5225 (2023) 17

    2023

  66. [2011]

    3153–3160

    pp. 3153–3160. IEEE (2011)

    2011

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.