The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Improving Multiple Pedestrian Tracking by Track Management and Occlusion Handling
Abstract
Multi-pedestrian trackers perform well when targets are clearly visible making the association task quite easy. However, when heavy occlusions are present, a mechanism to re-identify persons is needed. The common approach is to extract visual features from new detections and compare them with the features of previously found tracks. Since those detections can have substantial overlaps with nearby targets – especially in crowded scenarios – the extracted features are insufficient for a reliable re-identification. In contrast, we propose a novel occlusion handling strategy that explicitly models the relation between occluding and occluded tracks outperforming the feature-based approach, while not depending on a separate re-identification network. Furthermore, we improve the track management of a regression-based method in order to bypass missing detections and to deal with tracks leaving the scene at the border of the image. Finally, we apply our tracker in both temporal directions and merge tracklets belonging to the same target, which further enhances the performance. We demonstrate the effectiveness of our tracking components with ablative experiments and surpass the state-of-the-art methods on the three popular pedestrian tracking benchmarks MOT16, MOT17, and MOT20.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939
High-Speed Tracking with Kernelized Correlation Filters
Henriques, J. F., Caseiro, R., Martins, P., & Batista, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2345390 · Cited by 5,753
Simple online and realtime tracking with a deep association metric
Wojke, N., Bewley, A., & Paulus, D. (2017) · 2017 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2017.8296962 · Cited by 5,286
Simple online and realtime tracking
Bewley, A., Ge, Z., Ott, L., Ramos, F., & Upcroft, B. (2016) · 2016 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2016.7533003 · Cited by 4,433
Performance Measures and a Data Set for Multi-target, Multi-camera Tracking
Ristani, E., Solera, F., Zou, R., Cucchiara, R., & Tomasi, C. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48881-3_2 · Cited by 3,104
MMDetection: Open MMLab Detection Toolbox and Benchmark
Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Xu, J., et al. (2019) · arXiv · doi:10.48550/arxiv.1906.07155 · Cited by 2,227
Zhou, X., Koltun, V., & Krähenbühl, P. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58548-8_28 · Cited by 1,428
Cited by
Sorted by citations
Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking
Cao, J., Pang, J., Weng, X., Khirodkar, R., & Kitani, K. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00934 · Cited by 1,115
TrackFormer: Multi-Object Tracking with Transformers
Meinhardt, T., Kirillov, A., Leal-Taixe, L., & Feichtenhofer, C. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00864 · Cited by 1,081
Single-Model and Any-Modality for Video Object Tracking
Wu, Z., Zheng, J., Ren, X., Vasluianu, F.-A., Ma, C., Paudel, D. P., Gool, L. V., & Timofte, R. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01812 · Cited by 143
“While traditional RGB trackers have shown promise in general settings, they often struggle with challenging scenarios such as occlusions [40, 47], low visibility [83, 92], or fast-moving objects [10, 48].”
BackgroundSimple Cues Lead to a Strong Multi-Object Tracker
Seidenschwarz, J., Brasó, G., Serrano, V. C., Elezi, I., & Leal-Taixé, L. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01327 · Cited by 133
“Even new learning-based trackers still rely on motion priors [4,52,63,80] or appearance cues, which may include external reid models [4,63].”
BackgroundRobust Multi-Drone Multi-Target Tracking to Resolve Target Occlusion: A Benchmark
Liu, Z., Shang, Y., Li, T., Chen, G., Wang, Y., Hu, Q., & Zhu, P. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3234822 · Cited by 113
“Apart from that, Limited by the lack of supplementary information, single camera MOT has no advantage to address occlusion circumstances, and some priors predictions are always performed for occlusions [52], [53], [54].”
Background
Metadata
- Authors
- Stadler, D., & Beyerer, J.
- Affiliations
- Karlsruhe Institute of Technology (DE)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 10953-10962
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3175782503
- Cited by
- 147
- References
- 41
- Citations by year
- 2026: 10 · 2025: 22 · 2024: 35 · 2023: 37 · 2022: 39 · 2021: 4
- FWCI
- 9.40
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- multi-pedestrian tracking, occlusion handling, track management, person re-identification, track association, missing detection, crowded scenes
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