The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
GSM: Graph Similarity Model for Multi-Object Tracking
Open accessAbstract
The popular tracking-by-detection paradigm for multi-object tracking (MOT) focuses on solving data association problem, of which a robust similarity model lies in the heart. Most previous works make effort to improve feature representation for individual object while leaving the relations among objects less explored, which may be problematic in some complex scenarios. In this paper, we focus on leveraging the relations among objects to improve robustness of the similarity model. To this end, we propose a novel graph representation that takes both the feature of individual object and the relations among objects into consideration. Besides, a graph matching module is specially designed for the proposed graph representation to alleviate the impact of unreliable relations. With the help of the graph representation and the graph matching module, the proposed graph similarity model, named GSM, is more robust to the occlusion and the targets sharing similar appearance. We conduct extensive experiments on challenging MOT benchmarks and the experimental results demonstrate the effectiveness of the proposed method.
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“Furthermore, the frame association with track queries avoids postprocessing with heuristic greedy matching procedures [69] or additional graph optimization [29].”
From TrackFormer: Multi-Object Tracking with Transformers (2022) · cites this paper as [29]
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“These approaches achieve track association implicitly, but provide top performance only by relying either on additional graph optimization [6, 29] or motion and appearance models [4].”
From TrackFormer: Multi-Object Tracking with Transformers (2022) · cites this paper as [29]
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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
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
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
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
Social force model for pedestrian dynamics.
Helbing, D., & Molnár, P. (1995) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.51.4282 · Cited by 7,299
Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics
Bernardin, K., & Stiefelhagen, R. (2008) · EURASIP Journal on Image and Video Processing · doi:10.1155/2008/246309 · Cited by 2,918
You'll never walk alone: Modeling social behavior for multi-target tracking
Pellegrini, S., Ess, A., Schindler, K., & van Gool, L. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459260 · Cited by 1,683 · cited 2×
“Many early works extracted hand-craft appearance feature such as raw pixel template [ Yamaguchi et al. , 2011; Pellegrini et al. , 2009 ] , color histogram [ Izadinia et al. , 2012; Xiang et al. , 2015 ] , HOG [ Izadinia et al. , 2012; Kuo et al. , 2010 ] and so on.”
BackgroundMOT16: A Benchmark for Multi-Object Tracking
Milan, A., Leal-Taixe, L., Reid, I., Roth, S., & Schindler, K. (2016) · arXiv · doi:10.48550/arxiv.1603.00831 · Cited by 1,309
Tracking Without Bells and Whistles
Bergmann, P., Meinhardt, T., & Leal-Taixe, L. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00103 · Cited by 1,110
Learning to Track: Online Multi-object Tracking by Decision Making
Yu, X., Alahi, A., & Savarese, S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.534 · Cited by 684
Research works printed but not linked: 1
- Hamid Izadinia, Imran Saleemi, Wen- hui Li, and Mubarak Shah. 2 t: multiple people multiple parts tracker. In ECCV, pages 100-114. Springer, 2012.
Cited by
Sorted by citations
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
TransCenter: Transformers With Dense Representations for Multiple-Object Tracking
Xu, Y., Ban, Y., Delorme, G., Gan, C., Rus, D. L., & Alameda-Pineda, X. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3225078 · Cited by 296
SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking
Pang, Z., Li, Z., & Wang, N. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25056-9_43 · Cited by 167
He, J., Huang, Z., Wang, N., & Zhang, Z. (2021) · 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/cvpr46437.2021.00526 · Cited by 153
Occluded Video Instance Segmentation: A Benchmark
Qi, J., Gao, Y., Hu, Y. G., Wang, X., Liu, X., Bai, X., Belongie, S., Yuille, A., Torr, P. H. S., & Bai, S. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01629-1 · Cited by 191
Metadata
- Authors
- Liu, Q., Chu, Q., Liu, B., & Yu, N.
- Affiliations
- University of Science and Technology of China (CN)
- Corresponding author
- Chu, Qi
- Venue
- Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
- Published
- 2020-07
- Pages
- 530-536
- OpenAlex
- W3034240185
- Cited by
- 123
- References
- 27
- Citations by year
- 2026: 6 · 2025: 17 · 2024: 23 · 2023: 28 · 2022: 22 · 2021: 27
- FWCI
- 5.94
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- Funders
- Chinese Academy of Sciences (CXJJ-17S044)
- Fundamental Research Funds for the Central Universities (WK2100330002, WK3480000005)
- 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 › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Keywords
- multi-target tracking, graph representation, object relations, graph matching, data association, tracking-by-detection, occlusion robustness, visual similarity
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