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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Video K-Net: A Simple, Strong, and Unified Baseline for Video Segmentation
Li, X., Zhang, W., Pang, J., Chen, K., Cheng, G., Tong, Y., & Loy, C. C. (2022) · 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/cvpr52688.2022.01828 · Cited by 89 · see citing works
Self-citationLooking Beyond Two Frames: End-to-End Multi-Object Tracking Using Spatial and Temporal Transformers
Zhu, T., Hiller, M., Ehsanpour, M., Ma, R., Drummond, T. W., Reid, I. D., & Rezatofighi, H. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3213073 · Cited by 82 · see citing works
Detection Recovery in Online Multi-Object Tracking with Sparse Graph Tracker
Hyun, J., Kang, M., Wee, D., & Yeung, D. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00483 · Cited by 78 · see citing works
OVTrack: Open-Vocabulary Multiple Object Tracking
Li, S., Fischer, T., Ke, L., Ding, H., Danelljan, M., & Yu, F. (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/cvpr52729.2023.00539 · Cited by 77 · see citing works
Self-citationMultiple Object Tracking as ID Prediction
Gao, R., Qi, J., & Wang, L. (2025) · 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/cvpr52734.2025.02596 · Cited by 76 · see citing works
TransCenter: Transformers with Dense Queries for Multiple-Object TrackingPreprint
Xu, Y., Ban, Y., Delorme, G., Gan, C., Rus, D. L., & Alameda-Pineda, X. (2021) · arXiv (Cornell University) · Cited by 72 · see citing works
STDFormer: Spatial-Temporal Motion Transformer for Multiple Object Tracking
Hu, M., Zhu, X., Wang, H., Cao, S., Liu, C., & Song, Q. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3263884 · Cited by 71 · see citing works
Multi-Object Pedestrian Tracking Using Improved YOLOv8 and OC-SORT
Xiao, X. P., & Feng, X. (2023) · Sensors · doi:10.3390/s23208439 · Cited by 74 · see citing works
Haruna, Y., Qin, S., Chukkol, A. H. A., Yusuf, A. A., Bello, I., & Lawan, A. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110057 · Cited by 95 · see citing works
One More Check: Making “Fake Background” Be Tracked Again
Liang, C., Zhang, Z., Xue, Z., Li, B., & Hu, W. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20045 · Cited by 65 · see citing works
Yu, E., Li, Z., & Han, S. (2022) · 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/cvpr52688.2022.00863 · Cited by 63 · see citing works
TripletTrack: 3D Object Tracking using Triplet Embeddings and LSTM
Marinello, N., Proesmans, M., & Gool, L. V. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00496 · Cited by 62 · see citing works
SimpleTrack: Rethinking and Improving the JDE Approach for Multi-Object Tracking
Li, J., Ding, Y., Wei, H., Zhang, Y., & Lin, W. (2022) · Sensors · doi:10.3390/s22155863 · Cited by 60 · see citing works
Split and Connect: A Universal Tracklet Booster for Multi-Object Tracking
Wang, G., Wang, Y., Gu, R., Hu, W., & Hwang, J. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3140919 · Cited by 59 · see citing works
Uniformity and deformation: A benchmark for multi-fish real-time tracking in the farming
Huang, J., Yu, X., An, D. U., Ning, X., Liu, J., & Tiwari, P. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125653 · Cited by 60 · see citing works
Cao, X., Zheng, Y., Yao, Y., Qin, H., Cao, X., & Guo, S. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3526066 · Cited by 58 · see citing works
Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation
Ke, L., Li, X., Danelljan, M., Tai, Y., Tang, C., & Yu, F. (2021) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000517118 · Cited by 59 · see citing works
Self-citationAnimalTrack: A Benchmark for Multi-Animal Tracking in the Wild
Zhang, L., Gao, J., Xiao, Z., & Fan, H. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01711-8 · Cited by 57 · see citing works
Tracking Objects as Pixel-Wise Distributions
Zhao, Z., Wu, Z., Zhuang, Y., Li, B., & Jia, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20047-2_5 · Cited by 58 · see citing works
A survey of autonomous robots and multi-robot navigation: Perception, planning and collaboration
Chen, W., Chi, W., Ji, S., Ye, H., Liu, J., Jia, Y., Yu, J., & Cheng, J. (2024) · Biomimetic Intelligence and Robotics · doi:10.1016/j.birob.2024.100203 · Cited by 65 · see citing works
Tsai, C., Shen, G.-Y., & Nisar, H. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105770 · Cited by 54 · see citing works
MotionTrack: Learning motion predictor for multiple object tracking
Xiao, C., Cao, Q., Zhong, Y., Lan, L., Zhang, X., Luo, Z., & Tao, D. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106539 · Cited by 56 · see citing works
SoccerTrack: A Dataset and Tracking Algorithm for Soccer with Fish-eye and Drone Videos
Scott, A., Uchida, I., Onishi, M., Kameda, Y., Fukui, K., & Fujii, K. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00401 · Cited by 52 · see citing works
Multi-object tracking review: retrospective and emerging trend
Guan, Z., Wang, Z., Zhang, G., Li, L., Zhang, M., Shi, Z., & Jiang, N. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11212-y · Cited by 58 · see citing works
Dynamic center point learning for multiple object tracking under Severe occlusions
Hu, Y., Niu, A., Sun, J., Zhu, Y., Yan, Q., Dong, W., Woźniak, M., & Zhang, Y. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112130 · Cited by 49 · see citing works
A Multi-Object Tracking Algorithm With Center-Based Feature Extraction and Occlusion Handling
Cao, Z., Li, J., Zhang, D., Zhou, M., & Abusorrah, A. M. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3229978 · Cited by 45 · see citing works
Matching Anything by Segmenting Anything
Li, S., Ke, L., Danelljan, M., Piccinelli, L., Segù, M., Gool, L. V., & Yu, F. (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/cvpr52733.2024.01794 · Cited by 44 · see citing works
Self-citationTracking Every Thing in the Wild
Li, S., Danelljan, M., Ding, H., Huang, T. E., & Yu, F. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20047-2_29 · Cited by 42 · see citing works
Self-citationY-BGD: Broiler counting based on multi-object tracking
Li, X., Zhao, Z., Wu, J., Huang, Y., Wen, J., Sun, S., Xie, H., Sun, J., & Gao, Y. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107347 · Cited by 41 · see citing works
Self-citationTowards Generalizable Multi-Object Tracking
Qin, Z., Wang, L., Zhou, S., Fu, P., Hua, G., & Tang, W. (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/cvpr52733.2024.01797 · Cited by 41 · see citing works
Box-Grained Reranking Matching for Multi-Camera Multi-Target Tracking
Yang, X., Ye, J., Lu, J., Gong, C., Jiang, M., Lin, X., Zhang, W., Tan, X., Li, Y., Ye, X., et al. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00349 · Cited by 40 · see citing works
BPMTrack: Multi-Object Tracking With Detection Box Application Pattern Mining
Gao, Y., Xu, H., Li, J., & Gao, X. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3364828 · Cited by 39 · see citing works
Occlusion-Aware Instance Segmentation Via BiLayer Network Architectures
Ke, L., Tai, Y., & Tang, C. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3246174 · Cited by 38 · see citing works
Yolo-3DMM for Simultaneous Multiple Object Detection and Tracking in Traffic Scenarios
Liu, L., Song, X., Song, H., Sun, S., Han, X.-F., Akhtar, N., & Mian, A. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3360875 · Cited by 38 · see citing works
Multi-Object Tracking and Segmentation Via Neural Message Passing
Brasó, G., Cetintas, O., & Leal-Taixé, L. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01678-6 · Cited by 38 · see citing works
MambaTrack: A Simple Baseline for Multiple Object Tracking with State Space Model
Xiao, C., Cao, Q., Luo, Z., & Lan, L. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680944 · Cited by 41 · see citing works
Collaborative Multi-Object Tracking With Conformal Uncertainty Propagation
Su, S., Han, S., Li, Y., Zhang, Z., Feng, C., Ding, C., & Miao, F. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3364450 · Cited by 36 · see citing works
EgoHumans: An Egocentric 3D Multi-Human Benchmark
Khirodkar, R., Bansal, A., Ma, L., Newcombe, R. A., Vo, M., & Kitani, K. (2023) · IEEE International Conference on Computer Vision · doi:10.1109/iccv51070.2023.01814 · Cited by 81 · see citing works
VNVC: A Versatile Neural Video Coding Framework for Efficient Human-Machine Vision
Sheng, X., Li, L., Liu, D., & Li, H. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3356548 · Cited by 34 · see citing works
PolyphonicFormer: Unified Query Learning for Depth-Aware Video Panoptic Segmentation
Yuan, H., Li, X., Yang, Y., Cheng, G., Zhang, J., Tong, Y., Zhang, L., & Tao, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19812-0_34 · Cited by 34 · see citing works
Self-citationInteractive Multi-Scale Fusion of 2D and 3D Features for Multi-Object Vehicle Tracking
Wang, G., Peng, C., Gu, Y., Zhang, J., & Wang, H. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3275954 · Cited by 34 · see citing works
Liang, T., Li, B., Wang, M., Tan, H., & Luo, Z. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3227814 · Cited by 33 · see citing works
Decode-MOT: How Can We Hurdle Frames to Go Beyond Tracking-by-Detection?
Lee, S.-H., Park, D.-H., & Bae, S. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3298538 · Cited by 33 · see citing works
Temporal-Spatial Feature Interaction Network for Multi-Drone Multi-Object Tracking
Wu, H., Sun, H., Ji, K., & Kuang, G. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3478758 · Cited by 33 · see citing works
SeaTrack: Rethinking Observation-Centric SORT for Robust Nearshore Multiple Object Tracking
Ding, J., Li, W., Yang, M., Zhao, Y., Pei, L., & Tian, A. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111091 · Cited by 32 · see citing works
Jiang, C., Wang, Z., Liang, H., & Wang, Y. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3286979 · Cited by 31 · see citing works
Jin, Y., Gao, F., Yu, J., Wang, J., & Shuang, F. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3249162 · Cited by 31 · see citing works
Gragnaniello, D., Greco, A., Saggese, A., Vento, M., & Vicinanza, A. (2023) · Sensors · doi:10.3390/s23084024 · Cited by 35 · see citing works
Beyond SOT: Tracking Multiple Generic Objects at Once
Mayer, C., Danelljan, M., Yang, M., Ferrari, V., Gool, L. V., & Kuznetsova, A. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00668 · Cited by 30 · see citing works
Individual Locating of Soccer Players from a Single Moving View
Maglo, A., Orcesi, A., Denize, J., & Pham, Q. (2023) · Italian National Conference on Sensors · doi:10.3390/s23187938 · Cited by 32 · see citing works
Mask-Free Video Instance Segmentation
Ke, L., Danelljan, M., Ding, H., Tai, Y., Tang, C., & Yu, F. (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/cvpr52729.2023.02189 · Cited by 30 · see citing works
Self-citationDeconfuseTrack: Dealing with Confusion for Multi-Object Tracking
Huang, C., Han, S., He, M., Zheng, W., & Wei, Y. (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/cvpr52733.2024.01825 · Cited by 28 · see citing works
MSA-MOT: Multi-Stage Association for 3D Multimodality Multi-Object Tracking
Zhu, Z., Nie, J., Wu, H., He, Z., & Gao, M. (2022) · Sensors · doi:10.3390/s22228650 · Cited by 29 · see citing works
Robot Person Following Under Partial Occlusion
Ye, H., Zhao, J., Pan, Y., Cherr, W., Li, H., & Zhang, H. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160738 · Cited by 28 · see citing works
Multi-Object Tracking by Self-supervised Learning Appearance Model
Huang, K., Lertniphonphan, K., Chen, F., Li, J., & Wang, Z. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00318 · Cited by 27 · see citing works
Kreuzberg, L., Zulfikar, I. E., Mahadevan, S., Engelmann, F., & Leibe, B. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25056-9_34 · Cited by 27 · see citing works
One-Shot Multiple Object Tracking With Robust ID Preservation
Lv, W., Zhang, N., Zhang, J., & Zeng, D. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3339609 · Cited by 26 · see citing works
Collaborative Tracking Learning for Frame-Rate-Insensitive Multi-Object Tracking
Liu, Y., Wu, J., & Fu, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00914 · Cited by 26 · see citing works
Contrastive Learning for Multi-Object Tracking with Transformers
Plaen, P.-F. D., Marinello, N., Proesmans, M., Tuytelaars, T., & Gool, L. V. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00672 · Cited by 26 · see citing works
Ding, H., Seenivasan, L., Killeen, B. D., Cho, S. M., & Unberath, M. (2024) · Artificial Intelligence Surgery · doi:10.20517/ais.2024.16 · Cited by 27 · see citing works
An end-to-end tracking method for polyp detectors in colonoscopy videos
Yu, T., Lin, N., Zhang, X., Pan, Y., Hu, H., Zheng, W., Liu, J., Hu, W., Duan, H., & Si, J. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102363 · Cited by 26 · see citing works
Online multi-object tracking with δ-GLMB filter based on occlusion and identity switch handling
Abbaspour, M., & Masnadi‐Shirazi, M. A. (2022) · Image and Vision Computing · doi:10.1016/j.imavis.2022.104553 · Cited by 27 · see citing works
Deng, K., Zhang, C., Chen, Z., Hu, W., Li, B., & Lu, F. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3238716 · Cited by 28 · see citing works
Liu, C., Li, H., & Wang, Z. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01933-4 · Cited by 25 · see citing works
- 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 · 2020: 1
- 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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