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Multi-object tracking has seen a lot of progress recently, albeit with substantial annotation costs for developing better and larger labeled datasets.
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Milan, A., Leal-Taixé, L., Schindler, K., Reid, I.: Joint tracking and segmentation of multiple targets. In: CVPR. pp. 5397–5406 (2015)
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Leal-Taixé, L., Canton-Ferrer, C., Schindler, K.: Learning by tracking: Siamese cnn for robust target association. In: CVPR-W. pp. 33–40 (2016)
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Yang, F., Choi, W., Lin, Y.: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. In: CVPR. pp. 2129–2137 (2016)
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Yu, F., Li, W., Li, Q., Liu, Y., Shi, X., Yan, J.: Poi: Multiple object tracking with high performance detection and appearance feature. In: ECCV. pp. 36–42 (2016)
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Zhang, S., Gong, Y., Huang, J.B., Lim, J., Wang, J., Ahuja, N., Yang, M.H.: Tracking persons-of-interest via adaptive discriminative features. In: ECCV. pp. 415–433 (2016)
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Bochinski, E., Eiselein, V., Sikora, T.: High-speed tracking-by-detection without using image information. In: AVSS. pp. 1–6 (2017)
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Song, C., Huang, Y., Ouyang, W., Wang, L.: Mask-guided contrastive attention model for person re-identification. In: CVPR. pp. 1179–1188 (2018)
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Zhu, J., Yang, H., Liu, N., Kim, M., Zhang, W., Yang, M.H.: Online multi-object tracking with dual matching attention networks. In: ECCV. pp. 366–382 (2018)
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2017
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Manen, S., Gygli, M., Dai, D., Van Gool, L.: Pathtrack: Fast trajectory annotation with path supervision. In: ICCV. pp. 290–299 (2017)
2017
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Sadeghian, A., Alahi, A., Savarese, S.: Tracking the untrackable: Learning to track multiple cues with long-term dependencies. In: ICCV. pp. 300–311 (2017)
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Schulter, S., Vernaza, P., Choi, W., Chandraker, M.: Deep network flow for multi-object tracking. In: CVPR. pp. 6951–6960 (2017)
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Su, C., Li, J., Zhang, S., Xing, J., Gao, W., Tian, Q.: Pose-driven deep convolutional model for person re-identification. In: ICCV. pp. 3960–3969 (2017)
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Tang, S., Andriluka, M., Andres, B., Schiele, B.: Multiple people tracking by lifted multicut and person re-identification. In: CVPR. pp. 3539–3548 (2017)
2017
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Wang, S., Fowlkes, C.C.: Learning optimal parameters for multi-target tracking with contextual interactions. IJCV 122
2017
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Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric. In: ICIP. pp. 3645–3649 (2017)
2017
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2019
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Chu, P., Fan, H., Tan, C.C., Ling, H.: Online multi-object tracking with instance-aware tracker and dynamic model refreshment. In: WACV. pp. 161–170 (2019)
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Chu, P., Ling, H.: Famnet: Joint learning of feature, affinity and multi-dimensional assignment for online multiple object tracking. In: ICCV. pp. 6172–6181 (2019)
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Later among the works it cites.
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Lin, Y., Dong, X., Zheng, L., Yan, Y., Yang, Y.: A bottom-up clustering approach to unsupervised person re-identification. In: AAAI. pp. 8738–8745 (2019)
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Maksai, A., Fua, P.: Eliminating exposure bias and metric mismatch in multiple object tracking. In: CVPR. pp. 4639–4648 (2019)
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Later among the works it cites.
Xu, J., Cao, Y., Zhang, Z., Hu, H.: Spatial-temporal relation networks for multi-object tracking. In: ICCV. pp. 3988–3998 (2019)
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Zheng, Z., Yang, X., Yu, Z., Zheng, L., Yang, Y., Kautz, J.: Joint discriminative and generative learning for person re-identification. In: CVPR. pp. 2138–2147 (2019)
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2019
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Zhou, K., Yang, Y., Cavallaro, A., Xiang, T.: Omni-scale feature learning for person re-identification. In: ICCV. pp. 3702–3712 (2019)
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Brasó, G., Leal-Taixé, L.: Learning a neural solver for multiple object tracking. In: CVPR (2020)
2020
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Ciaparrone, G., Sánchez, F.L., Tabik, S., Troiano, L., Tagliaferri, R., Herrera, F.: Deep learning in video multi-object tracking: A survey. Neurocomputing (2020)
2020
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2020
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Xu, Y., Osep, A., Ban, Y., Horaud, R., Leal-Taixé, L., Alameda-Pineda, X.: How to train your deep multi-object tracker. In: CVPR (2020)
2020
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2020
Closest in time.
2020
Closest in time.