Online Multi-Object Tracking with Dual Matching Attention Networks
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For MOT, attention has only been used to associate a given set of object detections, not tackling the detection and tracking problem jointly.
“For MOT, attention has only been used to associate a given set of object detections [11,70], not tackling the detection and tracking problem jointly.”
From TrackFormer: Multi-Object Tracking with Transformers (2022) · cites this paper as [70]
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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 · see citing works
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581 · see citing works
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 · see citing works
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408 · see citing works
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248 · see citing works
Residual Attention Network for Image Classification
Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., & Tang, X. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.683 · Cited by 4,073 · see citing works
Wu, Y., Lim, J., & Yang, M.-H. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2388226 · Cited by 3,371 · cited 1× · see citing works
“The ECO tracker achieves the state-of-the-art performance on visual tracking benchmarks [25,52,38,33] and its fast variant ECO-HC based on hand-crafted features (HOG [11] and Color Names [49]) operates at 60 frames per second (FPS) on a single CPU, which is suitable for the online MOT task.”
MethodSelf-citationECO: Efficient Convolution Operators for Tracking
Danelljan, M., Bhat, G., Khan, F. S., & Felsberg, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.733 · Cited by 2,805 · see citing works
Globally-optimal greedy algorithms for tracking a variable number of objects
Pirsiavash, H., Ramanan, D., & Fowlkes, C. C. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995604 · Cited by 790 · see citing works
Multiple Hypothesis Tracking Revisited
Kim, C., Li, F., Ciptadi, A., & Rehg, J. M. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.533 · Cited by 666 · see citing works
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FairMOT: On the Fairness of Detection and Re-identification in Multiple Object Tracking
Zhang, Y., Wang, C., Wang, X., Zeng, W., & Liu, W. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01513-4 · Cited by 1,861 · see citing works
Towards Real-Time Multi-Object Tracking
Wang, Z., Zheng, L., Liu, Y., Li, Y., & Wang, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58621-8_7 · Cited by 1,195 · see citing works
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 · see citing works
“In this work, we introduce the tracking-by-attention paradigm which not only applies attention for data association [11, 67] but jointly performs tracking and detection.”
MethodDeep learning in video multi-object tracking: A survey
Ciaparrone, G., Luque Sánchez, F., Tabik, S., Troiano, L., Tagliaferri, R., & Herrera, F. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.11.023 · Cited by 665 · see citing works
Learning a Neural Solver for Multiple Object Tracking
Braso, G., & Leal-Taixe, L. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00628 · Cited by 504 · see citing works
MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking
Dendorfer, P., Os̆ep, A., Milan, A., Schindler, K., Cremers, D., Reid, I., Roth, S., & Leal-Taixé, L. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01393-0 · Cited by 374 · see citing works
TubeTK: Adopting Tubes to Track Multi-Object in a One-Step Training Model
Pang, B., Li, Y., Zhang, Y., Li, M., & Lu, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00634 · Cited by 303 · see citing works
GSM: Graph Similarity Model for Multi-Object Tracking
Liu, Q., Chu, Q., Liu, B., & Yu, N. (2020) · Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/74 · Cited by 123 · see citing works
Online multi-object tracking with pedestrian re-identification and occlusion processing
Zhang, X., Wang, X., & Gu, C. (2021) · The Visual Computer · doi:10.1007/s00371-020-01854-0 · Cited by 35 · see citing works
Vehicle Detection and Tracking from Surveillance Cameras in Urban Scenes
Messoussi, O., de Magalhães, F. G., Lamarre, F., Perreault, F., Sogoba, I., Bilodeau, G.-A., & Nicolescu, G. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-90436-4_15 · Cited by 9 · see citing works
- Authors
- Zhu, J., Yang, H., Liu, N., Kim, M., Zhang, W., & Yang, M.-H.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Northwestern Polytechnical University (CN)
- Massachusetts Institute of Technology (US)
- Google (United States) (US)
- Corresponding author
- Yang, Hua
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 379-396
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2895150009
- Cited by
- 377
- References
- 11
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 35 · 2023: 39 · 2022: 70 · 2021: 82 · 2020: 88 · 2019: 36 · 2018: 8
- FWCI
- 16.12
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (61521062, 1149783)
- Nvidia
- National Natural Science Foundation of China (18DZ1200102, 6152106 2, 61771303, 18DZ2270700, 61671289)
- Science and Technology Commission of Shanghai Municipality (61771303, 18DZ1200102, 18DZ2270700, 17DZ1205602)
- China Scholarship Council
- Shanghai Jiao Tong 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
- online multi-object tracking, data association, spatial attention, temporal attention, target interaction, identity preservation, visual object tracking
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