The DOI matches its Crossref record; 58 of the 59 references it cites were found in a registry or an index.
Quasi-Dense Similarity Learning for Multiple Object Tracking
Open accessAbstract
Similarity learning has been recognized as a crucial step for object tracking. However, existing multiple object tracking methods only use sparse ground truth matching as the training objective, while ignoring the majority of the informative regions on the images. In this paper, we present Quasi-Dense Similarity Learning, which densely samples hundreds of region proposals on a pair of images for contrastive learning. We can directly combine this similarity learning with existing detection methods to build Quasi-Dense Tracking (QDTrack) without turning to displacement regression or motion priors. We also find that the resulting distinctive feature space admits a simple nearest neighbor search at the inference time. Despite its simplicity, QD-Track outperforms all existing methods on MOT, BDD100K, Waymo, and TAO tracking benchmarks. It achieves 68.7 MOTA at 20.3 FPS on MOT17 without using external training data. Compared to methods with similar detectors, it boosts almost 10 points of MOTA and significantly decreases the number of ID switches on BDD100K and Waymo datasets. Our code and trained models are available at https://github.com/SysCV/qdtrack.
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Metadata
- Authors
- Pang, J., Qiu, L., Li, X., Chen, H., Li, Q., Darrell, T., & Yu, F.
- Affiliations
- Zhejiang University (CN)
- Georgia Institute of Technology (US)
- ETH Zurich (CH)
- Stanford University (US)
- University of California, Berkeley (US)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 164-173
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3165926952
- Cited by
- 480
- References
- 59
- Citations by year
- 2026: 49 · 2025: 84 · 2024: 127 · 2023: 134 · 2022: 77 · 2021: 8
- FWCI
- 28.29
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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
- multiple object tracking, similarity learning, contrastive learning, region proposal, nearest neighbor search, object detection, identity switching
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