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Modern multiple object tracking (MOT) systems usually follow the \emph{tracking-by-detection} paradigm.
Jiang, H., Fels, S., Little, J.J.: A linear programming approach for multiple object tracking. In: CVPR (2007)
2007
Earlier work this paper cites.
Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the clear mot metrics. Journal on Image and Video Processing 2008
2008
Earlier work this paper cites.
Ess, A., Leibe, B., Schindler, K., , van Gool, L.: A mobile vision system for robust multi-person tracking. In: CVPR (2008)
2008
Earlier work this paper cites.
Zhang, L., Li, Y., Nevatia, R.: Global data association for multi-object tracking using network flows. In: CVPR (2008)
2008
Earlier work this paper cites.
Dollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: A benchmark. In: CVPR (2009)
2009
Earlier work this paper cites.
Pirsiavash, H., Ramanan, D., Fowlkes, C.C.: Globally-optimal greedy algorithms for tracking a variable number of objects. In: CVPR (2011)
2011
Earlier work this paper cites.
Zamir, A.R., Dehghan, A., Shah, M.: Gmcp-tracker: Global multi-object tracking using generalized minimum clique graphs. In: ECCV (2012)
2012
Earlier work this paper cites.
Zamir, A.R., Dehghan, A., Shah, M.: Gmcp-tracker: Global multi-object tracking using generalized minimum clique graphs. In: ECCV (2012)
2012
Earlier work this paper cites.
Leal-Taixé, L., Fenzi, M., Kuznetsova, A., Rosenhahn, B., Savarese, S.: Learning an image-based motion context for multiple people tracking. In: CVPR (2014)
2014
Earlier work this paper cites.
Wen, L., Li, W., Yan, J., Lei, Z., Yi, D., Li, S.Z.: Multiple target tracking based on undirected hierarchical relation hypergraph. In: CVPR (2014)
2014
Earlier work this paper cites.
Choi, W.: Near-online multi-target tracking with aggregated local flow descriptor. In: ICCV (2015)
2015
Earlier work this paper cites.
Girshick, R.: Fast r-cnn. In: ICCV (2015)
2015
Earlier work this paper cites.
Kim, C., Li, F., Ciptadi, A., Rehg, J.M.: Multiple hypothesis tracking revisited. In: ICCV (2015)
2015
Earlier work this paper cites.
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. In: NIPS (2015)
2015
Earlier work this paper cites.
Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: A unified embedding for face recognition and clustering. In: CVPR (2015)
2015
Earlier work this paper cites.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: CVPR (2015)
2015
Earlier work this paper cites.
Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: ICIP (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Sohn, K.: Improved deep metric learning with multi-class n-pair loss objective. In: NIPS (2016)
2016
Cited alongside, same era.
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 workshop (2016)
2016
Cited alongside, same era.
Zagoruyko, S., Komodakis, N.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)
2016
Cited alongside, same era.
Cai, Z., Vasconcelos, N.: Cascade r-cnn: Delving into high quality object detection. In: CVPR (2018)
2018
Later among the works it cites.
Fang, K., Xiang, Y., Li, X., Savarese, S.: Recurrent autoregressive networks for online multi-object tracking. In: WACV (2018)
2018
Later among the works it cites.
Kendall, A., Gal, Y., Cipolla, R.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: CVPR (2018)
2018
Later among the works it cites.
Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. In: ECCV (2018)
2018
Later among the works it cites.
Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
2018
Later among the works it cites.
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Zheng, L., Bie, Z., Sun, Y., Wang, J., Su, C., Wang, S., Tian, Q.: Mars: A video benchmark for large-scale person re-identification. In: ECCV (2016)
2016
Cited alongside, same era.
2016
Cited alongside, same era.
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: ICCV (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR (2017)
2017
Cited alongside, same era.
Liu, Y., Yan, J., Ouyang, W.: Quality aware network for set to set recognition. In: CVPR (2017)
2017
Cited alongside, same era.
Newell, A., Huang, Z., Deng, J.: Associative embedding: End-to-end learning for joint detection and grouping. In: NIPS (2017)
2017
Cited alongside, same era.
Sener, O., Koltun, V.: Multi-task learning as multi-objective optimization. In: NIPS (2018)
2018
Later among the works it cites.
Sun, Y., Zheng, L., Yang, Y., Tian, Q., Wang, S.: Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline). In: ECCV (2018)
2018
Later among the works it cites.
Zhou, Z., Xing, J., Zhang, M., Hu, W.: Online multi-target tracking with tensor-based high-order graph matching. In: ICPR (2018)
2018
Later among the works it cites.
Zhu, J., Yang, H., Liu, N., Kim, M., Zhang, W., Yang, M.H.: Online multi-object tracking with dual matching attention networks. In: ECCV (2018)
2018
Later among the works it cites.
2019
Closest in time.
2019
Closest in time.
Mahmoudi, N., Ahadi, S.M., Rahmati, M.: Multi-target tracking using cnn-based features: Cnnmtt. Multimedia Tools and Applications 78
2019
Closest in time.
Sun, S., Akhtar, N., Song, H., Mian, A.S., Shah, M.: Deep affinity network for multiple object tracking. IEEE transactions on pattern analysis and machine intelligence (2019)
2019
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Voigtlaender, P., Krause, M., Osep, A., Luiten, J., Sekar, B.B.G., Geiger, A., Leibe, B.: Mots: Multi-object tracking and segmentation. In: CVPR (2019)
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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Li, J., Gao, X., Jiang, T.: Graph networks for multiple object tracking. In: CVPR (2020)
2020
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