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Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects across video frames.
H. W. Kuhn, “The hungarian method for the assignment problem,” Naval research logistics quarterly , vol. 2, no. 1-2, pp. 83–97, 1955
1955
Earlier work this paper cites.
R. E. Kalman, “A new approach to linear filtering and prediction problems,” J. Fluids Eng. , vol. 82, no. 1, pp. 35–45, 1960
1960
Earlier work this paper cites.
P. Felzenszwalb, D. McAllester, and D. Ramanan, “A discriminatively trained, multiscale, deformable part model,” in CVPR . IEEE, 2008, pp. 1–8
2008
Earlier work this paper cites.
A. Ess, B. Leibe, K. Schindler, and L. Van Gool, “A mobile vision system for robust multi-person tracking,” in CVPR . IEEE, 2008, pp. 1–8
2008
Earlier work this paper cites.
K. Bernardin and R. Stiefelhagen, “Evaluating multiple object tracking performance: the clear mot metrics,” EURASIP Journal on Image and Video Processing , vol. 2008, pp. 1–10, 2008
2008
Earlier work this paper cites.
A. Milan, S. Roth, and K. Schindler, “Continuous energy minimization for multitarget tracking,” IEEE transactions on pattern analysis and machine intelligence , vol. 36, no. 1, pp. 58–72, 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,” in CVPR , 2014, pp. 1218–1225
2014
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in ECCV . Springer, 2014, pp. 740–755
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,” in Advances in neural information processing systems , 2015, pp. 91–99
2015
Earlier work this paper cites.
X. Chen, K. Kundu, Y. Zhu, A. G. Berneshawi, H. Ma, S. Fidler, and R. Urtasun, “3d object proposals for accurate object class detection,” Advances in neural information processing systems , vol. 28, 2015
2015
Earlier work this paper cites.
A. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft, “Simple online and realtime tracking,” in ICIP . IEEE, 2016, pp. 3464–3468
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
F. Yang, W. Choi, and Y. Lin, “Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers,” in CVPR , 2016, pp. 2129–2137
2016
Earlier work this paper cites.
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler, and R. Urtasun, “Monocular 3d object detection for autonomous driving,” in CVPR , 2016, pp. 2147–2156
2016
Earlier work this paper cites.
E. Ristani, F. Solera, R. Zou, R. Cucchiara, and C. Tomasi, “Performance measures and a data set for multi-target, multi-camera tracking,” in ECCV . Springer, 2016, pp. 17–35
2016
Earlier work this paper cites.
N. Wojke, A. Bewley, and D. Paulus, “Simple online and realtime tracking with a deep association metric,” in ICIP . IEEE, 2017, pp. 3645–3649
2017
Earlier work this paper cites.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in ICCV , 2017, pp. 2961–2969
2017
Earlier work this paper cites.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in ICCV , 2017, pp. 2980–2988
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
S. Zhang, R. Benenson, and B. Schiele, “Citypersons: A diverse dataset for pedestrian detection,” in CVPR , 2017, pp. 3213–3221
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
E. Bochinski, V. Eiselein, and T. Sikora, “High-speed tracking-by-detection without using image information,” in AVSS . IEEE, 2017, pp. 1–6
2017
Earlier work this paper cites.
W. Luo, B. Yang, and R. Urtasun, “Fast and furious: Real time end-to-end 3d detection, tracking and motion forecasting with a single convolutional net,” in CVPR , 2018, pp. 3569–3577
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Z. Cai and N. Vasconcelos, “Cascade r-cnn: Delving into high quality object detection,” in CVPR , 2018, pp. 6154–6162
2018
Earlier work this paper cites.
L. Chen, H. Ai, Z. Zhuang, and C. Shang, “Real-time multiple people tracking with deeply learned candidate selection and person re-identification,” in ICME . IEEE, 2018, pp. 1–6
2018
Earlier work this paper cites.
J. Zhu, H. Yang, N. Liu, M. Kim, W. Zhang, and M.-H. Yang, “Online multi-object tracking with dual matching attention networks,” in Proceedings of the ECCV (ECCV) , 2018, pp. 366–382
2018
Earlier work this paper cites.
Y. Zhou and O. Tuzel, “Voxelnet: End-to-end learning for point cloud based 3d object detection,” in CVPR , 2018, pp. 4490–4499
2018
Earlier work this paper cites.
Y. Yan, Y. Mao, and B. Li, “Second: Sparsely embedded convolutional detection,” Sensors , vol. 18, no. 10, p. 3337, 2018
2018
Earlier work this paper cites.
A. Kundu, Y. Li, and J. M. Rehg, “3d-rcnn: Instance-level 3d object reconstruction via render-and-compare,” in CVPR , 2018, pp. 3559–3568
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
F. Meyer, T. Kropfreiter, J. L. Williams, R. Lau, F. Hlawatsch, P. Braca, and M. Z. Win, “Message passing algorithms for scalable multitarget tracking,” Proceedings of the IEEE , vol. 106, no. 2, pp. 221–259, 2018
2018
Earlier work this paper cites.
E. Baser, V. Balasubramanian, P. Bhattacharyya, and K. Czarnecki, “Fantrack: 3d multi-object tracking with feature association network,” in 2019 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2019, pp. 1426–1433
2019
Earlier work this paper cites.
X. Zhou, D. Wang, and P. Krähenbühl, “Objects as points,” arXiv preprint arXiv:1904.07850 , 2019
2019
Earlier work this paper cites.
J. Xu, Y. Cao, Z. Zhang, and H. Hu, “Spatial-temporal relation networks for multi-object tracking,” in ICCV , 2019, pp. 3988–3998
2019
Earlier work this paper cites.
P. Chu and H. Ling, “Famnet: Joint learning of feature, affinity and multi-dimensional assignment for online multiple object tracking,” in ICCV , 2019, pp. 6172–6181
2019
Earlier work this paper cites.
P. Bergmann, T. Meinhardt, and L. Leal-Taixe, “Tracking without bells and whistles,” in ICCV , 2019, pp. 941–951
2019
Earlier work this paper cites.
A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “Pointpillars: Fast encoders for object detection from point clouds,” in CVPR , 2019, pp. 12 697–12 705
2019
Earlier work this paper cites.
S. Shi, X. Wang, and H. Li, “Pointrcnn: 3d object proposal generation and detection from point cloud,” in CVPR , 2019, pp. 770–779
2019
Earlier work this paper cites.
Y. Wang, W.-L. Chao, D. Garg, B. Hariharan, M. Campbell, and K. Q. Weinberger, “Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving,” in CVPR , 2019, pp. 8445–8453
2019
Earlier work this paper cites.
X. Weng and K. Kitani, “Monocular 3d object detection with pseudo-lidar point cloud,” in CVPRW , 2019, pp. 0–0
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
2019
Cited alongside, same era.
H. Rezatofighi, N. Tsoi, J. Gwak, A. Sadeghian, I. Reid, and S. Savarese, “Generalized intersection over union: A metric and a loss for bounding box regression,” in CVPR , 2019, pp. 658–666
2019
Cited alongside, same era.
X. Weng, J. Wang, D. Held, and K. Kitani, “3d multi-object tracking: A baseline and new evaluation metrics,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 10 359–10 366
2020
Cited alongside, same era.
2020
Cited alongside, same era.
C. Reading, A. Harakeh, J. Chae, and S. L. Waslander, “Categorical depth distribution network for monocular 3d object detection,” in CVPR , 2021, pp. 8555–8564
2021
Later among the works it cites.
N. Benbarka, J. Schröder, and A. Zell, “Score refinement for confidence-based 3d multi-object tracking,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 8083–8090
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
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Z. Lu, V. Rathod, R. Votel, and J. Huang, “Retinatrack: Online single stage joint detection and tracking,” in CVPR , 2020, pp. 14 668–14 678
2020
Cited alongside, same era.
J. Peng, C. Wang, F. Wan, Y. Wu, Y. Wang, Y. Tai, C. Wang, J. Li, F. Huang, and Y. Fu, “Chained-tracker: Chaining paired attentive regression results for end-to-end joint multiple-object detection and tracking,” in ECCV . Springer, 2020, pp. 145–161
2020
Cited alongside, same era.
X. Zhou, V. Koltun, and P. Krähenbühl, “Tracking objects as points,” in ECCV . Springer, 2020, pp. 474–490
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Z. Wang, L. Zheng, Y. Liu, Y. Li, and S. Wang, “Towards real-time multi-object tracking,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XI 16 . Springer, 2020, pp. 107–122
2020
Cited alongside, same era.
2020
Cited alongside, same era.
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in ECCV . Springer, 2020, pp. 213–229
2020
Cited alongside, same era.
B. Shuai, A. Berneshawi, X. Li, D. Modolo, and J. Tighe, “Siammot: Siamese multi-object tracking,” in CVPR , 2021, pp. 12 372–12 382
2021
Later among the works it cites.
Z. Xu, W. Yang, W. Zhang, X. Tan, H. Huang, and L. Huang, “Segment as points for efficient and effective online multi-object tracking and segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44, no. 10, pp. 6424–6437, 2021
2021
Later among the works it cites.
H.-K. Chiu, J. Li, R. Ambruş, and J. Bohg, “Probabilistic 3d multi-modal, multi-object tracking for autonomous driving,” in ICRA , 2021, pp. 14 227–14 233
2021
Later among the works it cites.
Z. Ge, S. Liu, Z. Li, O. Yoshie, and J. Sun, “Ota: Optimal transport assignment for object detection,” in CVPR , 2021, pp. 303–312
2021
Later among the works it cites.
2021
Later among the works it cites.
J. Luiten, A. Osep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixé, and B. Leibe, “Hota: A higher order metric for evaluating multi-object tracking,” International journal of computer vision , vol. 129, no. 2, pp. 548–578, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
D. Park, R. Ambrus, V. Guizilini, J. Li, and A. Gaidon, “Is pseudo-lidar needed for monocular 3d object detection?” in ICCV , 2021, pp. 3142–3152
2021
Later among the works it cites.
2021
Later among the works it cites.
F. Yang, X. Chang, S. Sakti, Y. Wu, and S. Nakamura, “Remot: A model-agnostic refinement for multiple object tracking,” Image and Vision Computing , vol. 106, p. 104091, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
2022
Later among the works it cites.
Y. Zhang, P. Sun, Y. Jiang, D. Yu, F. Weng, Z. Yuan, P. Luo, W. Liu, and X. Wang, “Bytetrack: Multi-object tracking by associating every detection box,” in ECCV . Springer, 2022, pp. 1–21
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Zhang, C. Wang, X. Wang, W. Zeng, and W. Liu, “Robust multi-object tracking by marginal inference,” in ECCV . Springer, 2022, pp. 22–40
2022
Later among the works it cites.
T. Meinhardt, A. Kirillov, L. Leal-Taixe, and C. Feichtenhofer, “Trackformer: Multi-object tracking with transformers,” in CVPR , 2022, pp. 8844–8854
2022
Later among the works it cites.
2022
Later among the works it cites.
X. Ye, M. Shu, H. Li, Y. Shi, Y. Li, G. Wang, X. Tan, and E. Ding, “Rope3d: The roadside perception dataset for autonomous driving and monocular 3d object detection task,” in CVPR , 2022, pp. 21 341–21 350
2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Wang, V. C. Guizilini, T. Zhang, Y. Wang, H. Zhao, and J. Solomon, “Detr3d: 3d object detection from multi-view images via 3d-to-2d queries,” in Conference on Robot Learning . PMLR, 2022, pp. 180–191
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
H.-N. Hu, Y.-H. Yang, T. Fischer, T. Darrell, F. Yu, and M. Sun, “Monocular quasi-dense 3d object tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022
2022
Later among the works it cites.
T. Zhang, X. Chen, Y. Wang, Y. Wang, and H. Zhao, “Mutr3d: A multi-camera tracking framework via 3d-to-2d queries,” in CVPR , 2022, pp. 4537–4546
2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Zhang, C. Wang, X. Wang, W. Liu, and W. Zeng, “Voxeltrack: Multi-person 3d human pose estimation and tracking in the wild,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022
2022
Later among the works it cites.
X. Zhou, T. Yin, V. Koltun, and P. Krähenbühl, “Global tracking transformers,” in CVPR , 2022, pp. 8771–8780
2022
Later among the works it cites.
Z. Zhao, Z. Wu, Y. Zhuang, B. Li, and J. Jia, “Tracking objects as pixel-wise distributions,” in ECCV . Springer, 2022, pp. 76–94
2022
Later among the works it cites.
N. Marinello, M. Proesmans, and L. Van Gool, “Triplettrack: 3d object tracking using triplet embeddings and lstm,” in CVPR , 2022, pp. 4500–4510
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
X. Bai, Z. Hu, X. Zhu, Q. Huang, Y. Chen, H. Fu, and C.-L. Tai, “Transfusion: Robust lidar-camera fusion for 3d object detection with transformers,” in CVPR , 2022, pp. 1090–1099
2022
Later among the works it cites.
P. Li and J. Jin, “Time3d: End-to-end joint monocular 3d object detection and tracking for autonomous driving,” in CVPR , 2022, pp. 3885–3894
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
J.-N. Zaech, A. Liniger, D. Dai, M. Danelljan, and L. Van Gool, “Learnable online graph representations for 3d multi-object tracking,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 5103–5110, 2022
2022
Later among the works it cites.
A. Kim, G. Brasó, A. Ošep, and L. Leal-Taixé, “Polarmot: How far can geometric relations take us in 3d multi-object tracking?” in ECCV . Springer, 2022, pp. 41–58
2022
Later among the works it cites.
2022
Later among the works it cites.