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3D multi-object tracking (MOT) is an essential component for many applications such as autonomous driving and assistive robotics.
H. W Kuhn, “The Hungarian Method for the Assignment Problem,” Naval Research Logistics Quarterly , 1955
1955
Earlier work this paper cites.
R. Kalman, “A New Approach to Linear Filtering and Prediction Problems,” Journal of Basic Engineering , 1960
1960
Earlier work this paper cites.
L. Zhang, Y. Li, and R. Nevatia, “Global Data Association for Multi-Object Tracking Using Network Flows,” CVPR , 2008
2008
Earlier work this paper cites.
K. Bernardin and R. Stiefelhagen, “Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics,” Journal on Image and Video Processing , 2008
2008
Earlier work this paper cites.
H. Pirsiavash, D. Ramanan, and C. C. Fowlkes, “Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects,” CVPR , 2011
2011
Earlier work this paper cites.
A. Geiger, P. Lenz, and R. Urtasun, “Are We Ready for Autonomous Driving? the KITTI Vision Benchmark Suite,” CVPR , 2012
2012
Earlier work this paper cites.
C. Dicle, O. I. Camps, and M. Sznaier, “The Way They Move: Tracking Multiple Targets with Similar Appearance,” ICCV , 2013
2013
Earlier work this paper cites.
S. H. Bae and K. J. Yoon, “Robust Online Multi-Object Tracking Based on Tracklet Confidence and Online Discriminative Appearance Learning,” CVPR , 2014
2014
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” NIPS , 2015
2015
Earlier work this paper cites.
W. Choi, “Near-Online Multi-Target Tracking with Aggregated Local Flow Descriptor,” ICCV , 2015
2015
Earlier work this paper cites.
F. Solera, S. Calderara, and R. Cucchiara, “Towards the Evaluation of Reproducible Robustness in Tracking-by-Detection,” AVSS , 2015
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
J. H. Yoon, C. R. Lee, M. H. Yang, and K. J. Yoon, “Online Multi-Object Tracking via Structural Constraint Event Aggregation,” CVPR , 2016
2016
Earlier work this paper cites.
A. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft, “Simple Online and Realtime Tracking,” ICIP , 2016
2016
Cited alongside, same era.
S. Schulter, P. Vernaza, W. Choi, and M. Chandraker, “Deep Network Flow for Multi-Object Tracking,” CVPR , 2017
2017
Cited alongside, same era.
A. Osep, W. Mehner, M. Mathias, and B. Leibe, “Combined Image- and World-Space Tracking in Traffic Scenes,” ICRA , 2017
2017
Cited alongside, same era.
2018
Cited alongside, same era.
X. Weng, S. Wu, F. Beainy, and K. Kitani, “Rotational Rectification Network: Enabling Pedestrian Detection for Mobile Vision,” WACV , 2018
2018
Cited alongside, same era.
A. Patil, S. Malla, H. Gang, and Y.-T. Chen, “The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes,” ICRA , 2019
2019
Closest in time.
W. Zhang, H. Zhou, S. Sun, Z. Wang, J. Shi, and C. C. Loy, “Robust Multi-Modality Multi-Object Tracking,” ICCV , 2019
2019
Closest in time.
X. Weng and K. Kitani, “Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud,” ICCVW , 2019
2019
Closest in time.
B. Zhu, Z. Jiang, X. Zhou, Z. Li, and G. Yu, “Class-Balanced Grouping and Sampling for Point Cloud 3D Object Detection,” CVPR , 2019
2019
Closest in time.
2020
Closest in time.
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S. Sharma, J. A. Ansari, J. K. Murthy, and K. M. Krishna, “Beyond Pixels: Leveraging Geometry and Shape Cues for Online Multi-Object Tracking,” ICRA , 2018
2018
Cited alongside, same era.
D. Frossard and R. Urtasun, “End-to-End Learning of Multi-Sensor 3D Tracking by Detection,” ICRA , 2018
2018
Cited alongside, same era.
S. Scheidegger, J. Benjaminsson, E. Rosenberg, A. Krishnan, and K. Granstr, “Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filtering,” IV , 2018
2018
Cited alongside, same era.
A. Manglik, X. Weng, E. Ohn-bar, and K. M. Kitani, “Forecasting Time-to-Collision from Monocular Video: Feasibility, Dataset, and Challenges,” IROS , 2019
2019
Cited alongside, same era.
S. Shi, X. Wang, and H. Li, “PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud,” CVPR , 2019
2019
Cited alongside, same era.
H. Karunasekera, H. Wang, and H. Zhang, “Multiple Object Tracking with Attention to Appearance, Structure, Motion and Size,” IEEE Access , 2019
2019
Cited alongside, same era.
W. Tian, M. Lauer, and L. Chen, “Online Multi-Object Tracking Using Joint Domain Information in Traffic Scenarios,” IEEE Transactions on Intelligent Transportation Systems , 2019
2019
Cited alongside, same era.
2020
Closest in time.
E. Baser, V. Balasubramanian, P. Bhattacharyya, and K. Czarnecki, “FANTrack: 3D Multi-Object Tracking with Feature Association Network,” IV , 2020
2020
Closest in time.
X. Weng, Y. Wang, Y. Man, and K. Kitani, “GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with 2D-3D Multi-Feature Learning,” CVPR , 2020
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
L. Wen, D. Du, Z. Cai, Z. LeI, M.-C. Chang, H. Qi, J. Lim, M.-H. Yang, and S. Lyu, “UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking,” Computer Vision and Image Understanding , 2020
2020
Closest in time.