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Most modern multi-object tracking (MOT) systems follow the tracking-by-detection paradigm.
Gheissari, N., Sebastian, T., Hartley, R.: Person reidentification using spatiotemporal appearance. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06). vol. 2, pp. 1528–1535 (2006)
2006
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Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the clear mot metrics. Eurasip Journal on Image and Video Processing 2008
2008
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Ess, A., Leibe, B., Schindler, K., Gool, L.V.: A mobile vision system for robust multi-person tracking. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition. pp. 1–8 (2008)
2008
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Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: A benchmark. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. pp. 304–311 (2009)
2009
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Lin, T.Y., Maire, M., Belongie, S.J., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European Conference on Computer Vision. pp. 740–755 (2014)
2014
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Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. Proceedings - International Conference on Image Processing, ICIP 2016-Augus
2016
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he, k., zhang, x., ren, s., sun, j.: Deep residual learning for image recognition. CVPR (2016)
2016
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Kearnes, S., Mccloskey, K., Berndl, M., Pande, V., Riley, P.: Molecular graph convolutions: moving beyond fingerprints. Journal of Computer-Aided Molecular Design 30
2016
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2016
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Lan, L., Tao, D., Gong, C., Guan, N., Luo, Z.: Online multi-object tracking by quadratic pseudo-boolean optimization. IJCAI pp. 3396–3402 (2016)
2016
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Leal-Taixe, L., Canton-Ferrer, C., Schindler, K.: Learning by tracking: Siamese cnn for robust target association. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). vol. 1, pp. 418–425 (2016)
2016
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2016
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Tang, S., Andres, B., Andriluka, M., Schiele, B.: Multi-person tracking by multicut and deep matching. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9914 LNCS
2016
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Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs (2017)
2017
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He, K., Gkioxari, G., Dollár, P., Girshick, B.R.: Mask r-cnn. ICCV pp. 386–397 (2017)
2017
Cited alongside, same era.
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR 2017 : International Conference on Learning Representations 2017 (2017)
2017
Cited alongside, same era.
Qi, X., Liao, R., Jia, J., Fidler, S., Urtasun, R.: 3d graph neural networks for rgbd semantic segmentation. In: 2017 IEEE International Conference on Computer Vision (ICCV). pp. 5209–5218 (2017)
2017
Cited alongside, same era.
Sadeghian, A., Alahi, A., Savarese, S.: Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies. Proceedings of the IEEE International Conference on Computer Vision 2017-Octob
2017
Cited alongside, same era.
Yan, S., Xiong, Y., Lin, D., xiaoou Tang: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: AAAI-18 AAAI Conference on Artificial Intelligence. pp. 7444–7452 (2018)
2018
Later among the works it cites.
2019
Later among the works it cites.
Gao, J., Zhang, T., Xu, C.: Graph convolutional tracking. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4649–4659 (2019)
2019
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2019
Later among the works it cites.
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2017
Cited alongside, same era.
Xiao, T., Li, S., Wang, B., Lin, L., Wang, X.: Joint detection and identification feature learning for person search. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 3376–3385 (2017)
2017
Cited alongside, same era.
Zhang, S., Benenson, R., Schiele, B.: Citypersons: A diverse dataset for pedestrian detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4457–4465 (2017)
2017
Cited alongside, same era.
2018
Cited alongside, same era.
Chen, L., Ai, H., Zhuang, Z., Shang, C.: Real-time multiple people tracking with deeply learned candidate selection and person re-identification. In: 2018 IEEE International Conference on Multimedia and Expo (ICME). pp. 1–6 (2018)
2018
Cited alongside, same era.
Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
2018
Cited alongside, same era.
Shen, Y., Li, H., Yi, S., Chen, D., Wang, X.: Person re-identification with deep similarity-guided graph neural network. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 508–526 (2018)
2018
Cited alongside, same era.
Wang, Z., Chen, T., Ren, J., Yu, W., Cheng, H., Lin, L.: Deep reasoning with knowledge graph for social relationship understanding. In: IJCAI 2018: 27th International Joint Conference on Artificial Intelligence. pp. 1021–1028 (2018)
2018
Cited alongside, same era.
Ma, C., Li, Y., Yang, F., Zhang, Z., Zhuang, Y., Jia, H., Xie, X.: Deep association: End-to-end graph-based learning for multiple object tracking with conv-graph neural network. In: Proceedings of the 2019 on International Conference on Multimedia Retrieval. pp. 253–261 (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Yan, Y., Zhang, Q., Ni, B., Zhang, W., Xu, M., Yang, X.: Learning context graph for person search. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2158–2167 (2019)
2019
Later among the works it cites.
Zhou, X., Wang, D., Krähenbühl, P.: Objects as points (2019)
2019
Later among the works it cites.
Chu, Q., Ouyang, W., Liu, B., Zhu, F., Yu, N.: Dasot: A unified framework integrating data association and single object tracking for online multi-object tracking. In: AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence (2020)
2020
Closest in time.
Hornakova, A., Henschel, R., Rosenhahn, B., Swoboda, P.: Lifted disjoint paths with application in multiple object tracking. In: ICML 2020: 37th International Conference on Machine Learning (2020)
2020
Closest in time.
Lan, L., Wang, X., Hua, G., Huang, S.T., Tao, D.: Semi-online multi-people tracking by re-identification. International Journal of Computer Vision pp. 1–19 (2020)
2020
Closest in time.
Zhang, Y., Sheng, H., Wu, Y., Wang, S., Lyu, W., Ke, W., Xiong, Z.: Long-term tracking with deep tracklet association. IEEE Transactions on Image Processing pp. 1–1 (2020)
2020
Closest in time.
Zhang, Y., Wang, C., Wang, X., Zeng, W., Liu, W.: A simple baseline for multi-object tracking (2020)
2020
Closest in time.