Abstract
Person re-identification (re-ID) could automatically match the same pedestrian across multiple cameras. In this paper, we review three kinds of person re-ID methods with the generative model and comprehensively analyze the applications of the generative model. We perform comparison experiments to verify the performance of the generative model on DukeMTMC-reID, and reveal the generative model could produce meaningful training samples and learn more discriminative features for person re-ID.
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Acknowledgements
This work was supported by National Natural Science Foundation of China under Grant No. 61501327 and No. 61711530240, Natural Science Foundation of Tianjin under Grant No. 17JCZDJC30600, the Fund of Tianjin Normal University under Grant No. 135202RC1703, the Open Projects Program of National Laboratory of Pattern Recognition under Grant No. 201800002, the China Scholarship Council No. 201708120039 and No. 201708120040, the Tianjin Higher Education Creative Team Funds Program and Postgraduate Research Practice Project of Tianjin Normal University under Grant No. YZ1260021937.
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Zhang, Z., Si, T., Liu, S. (2020). Generative Model for Person Re-Identification: A Review. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Springer, Singapore. https://doi.org/10.1007/978-981-13-9409-6_174
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DOI: https://doi.org/10.1007/978-981-13-9409-6_174
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