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With the rise of end-to-end learning through deep learning, person detectors and re-identification (ReID) models have recently become very strong.
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R. Vezzani, D. Baltieri, and R. Cucchiara · 2013
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W. Luo, X. Zhao, and T. Kim · 2014
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E. Ristani and C. Tomasi · 2014
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C. Kim, F. Li, A. Ciptadi, and J. M. Rehg · 2015
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L. Leal-Taixé, A. Milan, I. Reid, S. Roth, and K. Schindler · 2015
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S. Rezatofighi, A. Milan, Z. Zhang, Q. Shi, A. Dick, and I. Reid · 2015
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Transferring rich feature hierarchies for robust visual tracking
N. Wang, S. Li, A. Gupta, and D.-Y. Yeung · 2015
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L. Zheng, L. Shen, L. Tian, S. Wang, J. Wang, and Q. Tian · 2015
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Social lstm: Human trajectory prediction in crowded spaces
A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese · 2016
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Online multi-target tracking using recurrent neural networks
A. Milan, S. Rezatofighi, A. Dick, K. Schindler, and I. Reid · 2016
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Performance measures and a data set for multi-target, multi-camera tracking
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Person Re-identification: Past, Present and Future
L. Zheng, Y. Yang, and A. G. Hauptmann · 2016
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A Discriminatively Learned CNN Embedding for Person Re-identification
Z. Zheng, L. Zheng, and Y. Yang · 2016
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In Defense of the Triplet Loss for Person Re-Identification
A. Hermans, L. Beyer, and B. Leibe · 2017
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Tracking the untrackable: Learning to track multiple cues with long-term dependencies
A. Sadeghian, A. Alahi, and S. Savarese · 2017
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