Cited by 480Video Surveillance and Tracking MethodsAutonomous Vehicle Technology and SafetyAdvanced Neural Network Applications

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 access

Pang, Jiangmiao, Qiu, Linlu, Li, Xia, Chen, Haofeng, Li, Qi, Darrell, Trevor, Yu, Fisher·2021·2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

DOI 10.1109/cvpr46437.2021.00023

Open this paper in CoChat — free account Open PDF

Abstract

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.

Integrity signals

Registry

Crossref · 10.1109/cvpr46437.2021.00023

Cites

58 of 58 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
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
Mask R-CNN
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
Momentum Contrast for Unsupervised Visual Representation Learning
He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00975 · Cited by 13,347
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020) · arXiv · doi:10.48550/arxiv.2002.05709 · Cited by 10,857
Representation Learning with Contrastive Predictive Coding
Oord, A. V. D., Li, Y., & Vinyals, O. (2018) · arXiv · doi:10.48550/arxiv.1807.03748 · Cited by 6,242

Show all 59 references

Sorted by citations

Items 201–300 of 479

Multi-object tracking by multi-feature fusion to associate all detected boxes
Bilakeri, S., & Karunakar, A. K. (2022) · Cogent Engineering · doi:10.1080/23311916.2022.2151553 · Cited by 8
SLAck: Semantic, Location, and Appearance Aware Open-Vocabulary Tracking
Li, S., Ke, L., Yang, Y.-H., Piccinelli, L., Segù, M., Danelljan, M., & Gool, L. V. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73383-3_1 · Cited by 10
Online Multi-object Tracking with YOLOv9 and DeepSORT Optimized by Optical Flow
Djarah, D., Benmakhlouf, A., Zidani, G., & Khettache, L. (2024) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.8770 · Cited by 11
RGB-T long-term tracking algorithm via local sampling and global proposals
Liu, J., Zhongqiang, L., & Xingzhong, X. (2022) · Signal Image and Video Processing · doi:10.1007/s11760-022-02187-2 · Cited by 8
Aircraft tracking in aerial videos based on fused RetinaNet and low‐score detection classification
Ahmed, M., Maher, A., & Bai, X. (2022) · IET Image Processing · doi:10.1049/ipr2.12665 · Cited by 8
People Tracking in Video Surveillance Systems Based on Artificial Intelligence
Nasry, A., Ez-Zahout, A., & Omary, F. (2023) · Journal of Automation Mobile Robotics & Intelligent Systems · doi:10.14313/jamris/1-2023/8 · Cited by 9
Addressing Challenges of Incorporating Appearance Cues Into Heuristic Multi-Object Tracker via a Novel Feature Paradigm
Liu, C., Li, H., Wang, Z., & Xu, R. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3468901 · Cited by 7
PMTrack: Multi-object Tracking with Motion-Aware
Guo, X., Zheng, Y., & Wang, D. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0960-4_26 · Cited by 9
Robust Multi-Object Tracking with pseudo-information guided motion and enhanced semantic vision
Zhang, Y., Wang, S., Fu, Z., Zhao, L., & Zhao, J. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126846 · Cited by 7
ArbiTrack: A Novel Multi-Object Tracking Framework for a Moving AAV to Detect and Track Arbitrarily Oriented Targets
Chen, Y., Wang, J., Zhou, Q., & Hu, H. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3543018 · Cited by 8
ETAP: Event-based Tracking of Any Point
Hamann, F., Gehrig, D., Febryanto, F., Daniilidis, K., & Gallego, G. (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/cvpr52734.2025.02532 · Cited by 10
Bilateral association tracking with parzen window density estimation
Xu, Y., Chen, Y., Zhang, Y., Zhu, Q., He, Y., & Sheng, H. (2022) · IET Image Processing · doi:10.1049/ipr2.12633 · Cited by 7
Who is partner: A new perspective on data association of multi-object tracking
Ding, Y., Sun, Y., & Li, Z. (2023) · Image and Vision Computing · doi:10.1016/j.imavis.2023.104737 · Cited by 7
Multi-Object Tracking Based on Re-Identification Enhancement and Associated Correction
Li, J., & Piao, Y. (2023) · Applied Sciences · doi:10.3390/app13179528 · Cited by 7
Self-Supervised Multi-Object Tracking for Autonomous Driving From Consistency Across Timescales
Lang, C., Braun, A., Schillingmann, L., & Valada, A. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3322089 · Cited by 7
Bridging Images and Videos: A Simple Learning Framework for Large Vocabulary Video Object Detection
Woo, S., Park, K., Oh, S. W., Kweon, I. S., & Lee, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19806-9_14 · Cited by 7
Trajectory‐BERT: Pre‐training and fine‐tuning bidirectional transformers for crowd trajectory enhancement
Li, L., Huang, T., Li, Y., & Li, P. (2023) · Computer Animation and Virtual Worlds · doi:10.1002/cav.2190 · Cited by 7
Design and Thermal Analysis of 2.5D and 3-D Integrated System of a CMOS Image Sensor and a Sparsity-Aware Accelerator for Autonomous Driving
Sharda, J., Manley, M., Kaul, A., Li, W., Bakir, M. S., & Yu, S. (2024) · IEEE Journal of the Electron Devices Society · doi:10.1109/jeds.2024.3354621 · Cited by 6
A Journey of a 1,000 Kernels Begins with a Single Step: A Retrospective of Deep Learning on GPUs
Davies, M., McDougall, I. L., Anandaraj, S., Machchhar, D., Jain, R., & Sankaralingam, K. (2024) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3620665.3640367 · Cited by 7
AHMOT: Adaptive Kalman Filtering and Hierarchical Data Association for 3-D Multiobject Tracking in IoT-Enabled Autonomous Vehicles
Jiang, C., Zhang, M., Wang, Y., & Zhang, A. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3546639 · Cited by 7
Similarity- and Quality-Guided Relation Learning for Joint Detection and Tracking
Feng, W., Bai, L., Yao, Y., Gan, W., Wu, W., & Ouyang, W. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3279670 · Cited by 6
Toward Enhanced Support for Ship Sailing
Cafaro, M., Epicoco, I., Pulimeno, M., & Sansebastiano, E. (2023) · IEEE Access · doi:10.1109/access.2023.3303808 · Cited by 6
S.T.A.R.-Track: Latent Motion Models for End-to-End 3D Object Tracking With Adaptive Spatio-Temporal Appearance Representations
Doll, S., Hanselmann, N., Schneider, L., Schulz, R., Enzweiler, M., & Lensch, H. P. A. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3342552 · Cited by 6
Multi-Object Tracking for Unmanned Aerial Vehicles Based on Multi-Frame Feature Fusion
Wen, J., Wang, D., Fang, J., Li, Y., & Xu, Z. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447050 · Cited by 7
View adaptive multi-object tracking method based on depth relationship cues
Sun, H., Li, Y., Yang, G., Su, Z., & Luo, K. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01776-7 · Cited by 7
OccluTrack: Rethinking Awareness of Occlusion for Enhancing Multiple Pedestrian Tracking
Gao, J., Wang, Y., Yap, K., Garg, K., & Han, B. S. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3565334 · Cited by 7
Temporal Frame Filtering with Near-Pixel Compute for Autonomous Driving
Li, W., Wu, Q., Sharda, J., Chang, S., & Yu, S. (2022) · IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) · doi:10.1109/aicas54282.2022.9870011 · Cited by 6
A long-memory pedestrian target tracking algorithm incorporating spatiotemporal trajectory feature enhancement model
Cao, J., Zhang, J., Li, B., Gao, L., & An, J. (2023) · Digital Signal Processing · doi:10.1016/j.dsp.2023.103929 · Cited by 6
Online multiple object tracking with enhanced Re‐identification
Yang, W., Jiang, Y., Wen, S., & Fan, Y. (2023) · IET Computer Vision · doi:10.1049/cvi2.12191 · Cited by 6
CSMOT: Make One-Shot Multi-Object Tracking in Crowded Scenes Great Again
Hou, H., Shen, C., Zhang, X., & Gao, W. (2023) · Sensors · doi:10.3390/s23073782 · Cited by 6
M5: Multi-modal Multi-task Model Mapping on Multi-FPGA with Accelerator Configuration Search
Kamath, A. K., Abi-Karam, S., Bhat, A., & Hao, C. (2023) · Design, Automation, and Test in Europe · doi:10.23919/date56975.2023.10136962 · Cited by 6
1st Place Solution for YouTubeVOS Challenge 2021:Video Instance SegmentationPreprint
Nguyen, T. C., Tang, T. N., Phan, N. L. H., Nguyen, C. H., Yamazaki, M., & Yamanaka, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.06649 · Cited by 6
Tracking Instances as QueriesPreprint
Yang, S., Fang, Y., Wang, X., Li, Y., Shan, Y., Feng, B., & Liu, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.11963 · Cited by 6
Weakly Supervised Video Individual Counting
Liu, X., Li, G., Qi, Y., Yan, Z., Han, Z., van den Hengel, A., Yang, M., & Huang, Q. (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/cvpr52733.2024.01819 · Cited by 5
DETrack: Depth information is predictable for tracking
Zhao, W., Jiang, Y., Gao, Y., Li, J., & Gao, X. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128906 · Cited by 5
Re-identification assistance and multi-stage association for pedestrian multi-object tracking
Li, Y., Li, Z., Wu, L., Liu, H., Min, J., & Wang, X. (2025) · Scientific Reports · doi:10.1038/s41598-025-07276-z · Cited by 5
EnBoT-SORT: Hierarchical fusion-association tracking with pseudo-sample generation for dense thermal infrared UAVs
Guo, J., Zhan, W., Chen, Y., Zhu, D., Jiang, Y., Xu, X., & Han, D. (2025) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2025.12.001 · Cited by 5
Enhancing UAV tracking: a focus on discriminative representations using contrastive instances
Wang, X., Zeng, D., Li, Y., Zou, M., Zhao, Q., & Li, S. (2024) · Journal of Real-Time Image Processing · doi:10.1007/s11554-024-01456-2 · Cited by 5
Robust pedestrian multi-object tracking in the intelligent bus environment
Wang, S., Guo, Y., & Li, Y. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad5c8b · Cited by 5
Multi-Object Tracking by Hierarchical Visual Representations
Cao, J., Pang, J., & Kitani, K. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611201 · Cited by 5
Self-citation
Batch-MOT: Batch-Enabled Real-Time Scheduling for Multiobject Tracking Tasks
Kang, D., Lee, S., Hong, C.-H., Lee, J., & Baek, H. (2024) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2024.3443002 · Cited by 5
SPAMming Labels: Efficient Annotations for the Trackers of Tomorrow
Cetintas, O., Meinhardt, T., Brasó, G., & Leal-Taixé, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73254-6_22 · Cited by 5
Automated Sperm Tracking and Immobilization With a Clinically-Compatible XYZ Stage
Song, H., Chen, W., Shan, G., Dai, C., Yang, S., Jiang, A., Liu, H., Zhang, Z., & Sun, Y. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3529283 · Cited by 5
DragonTrack: Transformer-Enhanced Graphical Multi-Person Tracking in Complex Scenarios
Galoaa, B. M., Amraee, S., & Ostadabbas, S. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00621 · Cited by 5
OCAFI: observation centric appearance free interpolation technique for multi-object tracking
Bilakeri, S., & Kotegar, K. A. (2025) · Cogent Engineering · doi:10.1080/23311916.2025.2532808 · Cited by 5
End-to-End Network for Pedestrian Detection, Tracking and Re-Identification in Real-Time Surveillance System
Lei, M., Song, Y., Zhao, J., Wang, X., Lyu, J., Xu, J., & Yan, W. (2022) · Sensors · doi:10.3390/s22228693 · Cited by 7
Efficient combination graph model based on conditional random field for online multi-object tracking
Zhang, J., Zhang, X., Zhu, Z., & Deng, C. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00922-3 · Cited by 5
Online Multi-Object Tracking with Generalized IoU Gating Mechanism
Wang, Y., Huang, D., Shi, Y., Chen, L., & Chen, L. (2022) · China Automation Congress (CAC) · doi:10.1109/cac57257.2022.10055877 · Cited by 5
Enriching Phrases with Coupled Pixel and Object Contexts for Panoptic Narrative Grounding
Hui, T., Ding, Z., Huang, J., Wei, X., Wei, X. K., Dai, J., Han, J., & Liu, S. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/99 · Cited by 8
FSTrack: One-Shot Multi-Object Tracking Algorithm Based on Feature Enhancement and Similarity Estimation
He, B., Yuan, L., & Lv, K. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3371331 · Cited by 5
ReIDTracker_Sea: Multi-Object Tracking in Maritime Computer Vision
Huang, K., Chong, W., Yang, H., Lertniphonphan, K., Xie, J., & Chen, F. (2024) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw60836.2024.00130 · Cited by 5
Rethinking Motion Estimation: An Outlier Removal Strategy in SORT for Multi-Object Tracking With Camera Moving
Min, Z., Hassan, G. M., & Jo, G. (2024) · IEEE Access · doi:10.1109/access.2024.3432156 · Cited by 5
LinkOcc: 3D Semantic Occupancy Prediction With Temporal Association
Ouyang, W., Xu, Z., Shen, B., Wang, J., & Xu, Y. D. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3486019 · Cited by 5
Walker: Self-supervised Multiple Object Tracking by Walking on Temporal Appearance Graphs
Segù, M., Piccinelli, L., Li, S., Gool, L. V., Yu, F., & Schiele, B. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73242-3_1 · Cited by 5
Self-citation
Multigranularity Localization Transformer With Collaborative Understanding for Referring Multiobject Tracking
Chen, J., Lin, J., Zhong, G., Yao, Y., & Li, Z. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3522698 · Cited by 5
Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
Cheng, X., Zhao, H., Deng, Y. X., & Shen, S. (2025) · Applied Sciences · doi:10.3390/app15020736 · Cited by 5
Multi-Vehicle Tracking Based on Monocular Camera in Driver View
Lyu, P., Wei, M., & Wu, Y. (2022) · Applied Sciences · doi:10.3390/app122312244 · Cited by 5
Driver Behavior in Response to Forward Collision Warnings Considering Driving Context
Zhao, Z., Pipkorn, L., Mehler, B. L., Reimer, B., & Gershon, P. (2024) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/10711813241276455 · Cited by 4
An Improved Multi-Object Tracking Algorithm Designed for Complex Environments
Liu, W., Yao, J., Jiang, F., & Wang, M. (2025) · Sensors · doi:10.3390/s25175325 · Cited by 4
KalmanFormer: Integrating a Deep Motion Model into SORT for Video Multi-Object Tracking
Hong, J., Li, Y., Yan, J., Wei, X., Xian, W., & Qin, Y. (2025) · Applied Sciences · doi:10.3390/app15179727 · Cited by 4
VOVTrack: Exploring the Potentiality in Raw Videos for Open-Vocabulary Multi-Object Tracking
Qian, Z., Han, R., Hou, J., Song, L., & Feng, W. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00701 · Cited by 4
A simple transformer-based baseline for crowd tracking with Sequential Feature Aggregation and Hybrid Group Training
Wang, C., Wu, Z., Ke, W., & Xiong, Z. (2024) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2024.104144 · Cited by 4
AMtrack: Anti-occlusion multi-object tracking algorithm
Liu, Z., Huang, X., Sun, J., & Zhang, X. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03547-w · Cited by 4
EnhanceCenter for improving point based tracking and rich feature representation
Yang, H., Park, S.-W., Jung, S.-H., & Sim, C.-B. (2025) · Scientific Reports · doi:10.1038/s41598-025-88924-2 · Cited by 4
Online Multi-Object Tracking Based on Record Confidence and Hierarchical Association for Cyber-Physical Social Intelligence
Yang, J., Feng, D., Gao, Y., & Liu, C. (2025) · Big Data Mining and Analytics · doi:10.26599/bdma.2025.9020024 · Cited by 5
Associate Everything Detected: Facilitating Tracking-by-Detection to the Unknown
Fang, Z., Liang, C., Zhou, X. S., Zhu, S., & Li, X. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3592524 · Cited by 6
Self-citation
PKF: Probabilistic Data Association Kalman Filter for Multi-Object Tracking
Cao, H., Pappas, G. J., & Atanasov, N. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3608646 · Cited by 9
Learning key lines for multi-object tracking
Li, Y., Ji, H., Chen, X., Yang, Y., & Lai, Y. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.103973 · Cited by 4
AETrack: An Efficient Approach for Online Multi-Object Tracking
Wang, X., Han, Y., Liu, Q., Li, J., Wang, B., Liu, H., & Chen, H. (2024) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv55156.2024.10588483 · Cited by 4
Self-citation
STT: Stateful Tracking with Transformers for Autonomous Driving
Jing, L., Yu, R., Xu, C., Zhao, Z., Sheng, S., Graber, C., Qi, C., Li, Q., Wu, S., Deng, H., et al. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610802 · Cited by 4
Self-citation
FusionTrack: An Online 3D Multi-object Tracking Framework Based on Camera-LiDAR Fusion
Zeng, W., Fan, J., Tian, X., Chu, H., & Gao, B. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802805 · Cited by 4
GroupTrack: Multi-Object Tracking by Using Group Motion Patterns
Xu, X., Ren, W., Sun, G., Ji, H., Gao, Y., & Liu, H. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802541 · Cited by 4
Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices
Zhang, Y., Kamath, A. K., Wu, Q., Fan, Z., Chen, W., Wang, Z., Chang, S., Liu, S., & Hao, C. (2023) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1145/3566097.3567864 · Cited by 5
Multi-Object Tracking with Adaptive Cost Matrix
Wang, M., Lit, B., Jiang, H., & Zhang, J. (2022) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp55362.2022.9948977 · Cited by 4
MOTCOM: The Multi-Object Tracking Dataset Complexity Metric
Pedersen, M., Haurum, J. B., Dendorfer, P., & Moeslund, T. B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20074-8_2 · Cited by 5
Visual Tracking With Reinforced Template Updating and Redetection Discriminator
Zhong, S., Sun, Y., Gong, S., Zhou, L., & Xie, G. (2022) · IEEE Journal on Miniaturization for Air and Space Systems · doi:10.1109/jmass.2022.3228339 · Cited by 4
$\text{Offset}^{3}\text{Net}$: Simple Joint 3-D Detection and Tracking With Three-Step Offset Learning
Sun, J., Ji, Y., He, J., Wu, F., & Sun, Y. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3290184 · Cited by 4
Cannot See the Forest for the Trees: Aggregating Multiple Viewpoints to Better Classify Objects in Videos
Hwang, S., Heo, M., Oh, S. W., & Kim, S. J. (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/cvpr52688.2022.01654 · Cited by 4
TGRMPT: A Head-Shoulder Aided Multi-Person Tracker and a New Large-Scale Dataset for Tour-Guide Robot
Wang, W., Lin, Z., Huang, M., Hu, S., Jin, T., Li, T., Wan, M., Gu, J. J., & Zhu, S. (2022) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio55434.2022.10011698 · Cited by 4
Composite Learning for Robust and Effective Dense Predictions
Kanakis, M., Huang, T. E., Brüggemann, D., Yu, F., & Gool, L. V. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00233 · Cited by 4
Self-citation
Occlusion-related graph convolutional neural network for multi-object tracking
Zhang, Y., Zheng, L., & Huang, Q. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105317 · Cited by 3
基于YOLOv8和准密集相似性学习的多目标跟踪方法
Wenwu, 曹. C., Daqun, 李. L., Zhijia, 吴. W., & Jianzhuo, 刘. L. (2025) · Chinese Journal of Lasers · doi:10.3788/cjl241243 · Cited by 3
Is a Pure Transformer Effective for Separated and Online Multi-Object Tracking?
Liu, C., Li, H., Wang, Z., & Xu, R. (2025) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3749105 · Cited by 5
Occlusion-robust multi-object tracking against viewpoint changing for UAV
Zhai, Y., Lu, D., Li, J., & Wang, Y. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132440 · Cited by 2
A spatiotemporal transformer with cross-frame encoding and trajectory-aware decoding for multi-target fish tracking
Li, Y., & Han, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-31686-8 · Cited by 4
Online visual multi-object tracking via blind super-resolution and clustering learning
Sun, Z., Lv, Z., Xiong, M., Hu, W., Wei, G., Zhou, J., He, T., & Meng, Q. (2026) · The Visual Computer · doi:10.1007/s00371-026-04382-5 · Cited by 3
VGT-MOT: visibility-guided tracking for online multiple-object tracking
Wang, S., Li, W., Wang, L., Xu, L., & Deng, Q. (2023) · Machine Vision and Applications · doi:10.1007/s00138-023-01398-y · Cited by 3
Cluster2Former: Semisupervised Clustering Transformers for Video Instance Segmentation
Fóthi, Á., Szlatincsán, A., & Somfai, E. (2024) · Sensors · doi:10.3390/s24030997 · Cited by 3
One-Stage Anchor-Free Online Multiple Target Tracking With Deformable Local Attention and Task-Aware Prediction
Hu, W., Wang, S., Zhou, Z., Gao, J. G., Li, Y., & Maybank, S. J. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3457886 · Cited by 3
DCTracker: Rethinking MOT in soccer events under dual views via cascade association
Hu, L., Zhang, J., Lv, W., Gong, Y., Wang, J., Zhang, J., & Zeng, D. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112528 · Cited by 3
Multi-Object Tracking Method Based on Domain Adaptation and Camera Motion Compensation
Zhang, Y., Da, F., & Zhou, H. (2025) · Electronics · doi:10.3390/electronics14112238 · Cited by 3
Mask Guided Spatial-Temporal Fusion Network for Multiple Object Tracking
Zhao, S., Wu, Y., Wang, S., Ke, W., & Sheng, H. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9898054 · Cited by 3
Label-Free Mammalian Cell Tracking Enhanced by Precomputed Velocity Fields
Han, Y., Yang, L., Shkolnikov, V., Xin, D., Barcelo, S. J., Allebach, J. P., & Delp, E. J. (2022) · International Conference on Machine Learning and Applications · doi:10.1109/icmla55696.2022.00057 · Cited by 3
Thermal Modeling of 2.5D Integrated Package of CMOS Image Sensor and FPGA for Autonomous Driving
Sharda, J., Manley, M., Kaul, A., Li, W., Bakir, M. S., & Yu, S. (2023) · IEEE Electron Devices Technology & Manufacturing Conference (EDTM) · doi:10.1109/edtm55494.2023.10102948 · Cited by 3
JDT-NAS: Designing Efficient Multi-Object Tracking Architectures for Non-GPU Computers
Chen, D., Shen, H., & Shen, Y. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3275813 · Cited by 3
Multi-Object Detection and Tracking Using Reptile Search Optimization Algorithm with Deep Learning
Alagarsamy, R., & Dhamodaran, M. (2023) · Symmetry · doi:10.3390/sym15061194 · Cited by 3
Robust Multi-Object Tracking With Local Appearance and Stable Motion Models
Hwang, J., Shim, K., Ko, K., Ha, N., & Kim, C. (2023) · IEEE Access · doi:10.1109/access.2023.3296731 · Cited by 3
TMTB: Transformer based multi-task branching multi-object tracking algorithm for wide-view scenes
Li, H., & Li, J. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17255-z · Cited by 3
Novel learning framework for optimal multi-object video trajectory tracking
Chen, S., Hu, X., Jiang, W., Zhou, W. J., & Ding, X. (2023) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2023.04.001 · Cited by 3
P-IoU: Accurate Motion Prediction Based Data Association for Multi-object Tracking
Wu, X., & Xu, J. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8073-4_37 · Cited by 2

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)
ORCID: Pang, Jiangmiao
Qiu, Linlu
Li, Xia
Chen, Haofeng
Li, Qi
Darrell, Trevor
Yu, Fisher
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)
DOI: 10.1109/cvpr46437.2021.00023
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
Licence: doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Quasi-Dense Similarity Learning for Multiple Object Tracking

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Citations

Cites

Cited by

Related

Metadata

Record

About these checks