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Video-based person re-identification by semi-supervised adaptive stepwise learning

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Abstract

Person re-identification (ReID) is mainly aimed at establishing correct identity correspondence among moving person collected by multiple cameras. Extending labeled data sets with pseudo-labels is one of the common methods of ReID. However, single evaluation standards and fixed screening pseudo-label methods make pseudo-labels gradually weaken their update rate. Based on that, we propose a semi-supervised adaptive stepwise learning (SSAS) method for accelerating the update of pseudo-labels. Using the concept of Kullback–Leibler divergence, a more global pseudo-label update idea (GPLU) is proposed, an evaluation criterion of pseudo-labels is designed to satisfy two conditions: The first is to use simple tracklets as pseudo-label data in the early stage, and the second is to gradually add complex and diverse tracklets as pseudo-label data in the iterative process. Our proposed adaptive pseudo-label screening strategy steadily improves the recognition accuracy of ReID. In addition, we conduct extensive experiments on canonical data sets and the evaluation results suggest the superiority of our method.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 61806206, 61772530), Natural Science Foundation of Jiangsu Province (Grant Nos. BK20180639, BK20201346).

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Quanshan, Xuzhou, Jiangsu, China

    Ding Ma, Yong Zhou, Jiaqi Zhao, Ying Chen & Rui Yao

  2. Xuzhou Guanglian Technology Co., Ltd, Xuzhou, Jiangsu, China

    Hao Chen

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  1. Ding Ma
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  2. Yong Zhou
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  3. Jiaqi Zhao
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  4. Ying Chen
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Correspondence to Yong Zhou.

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Ma, D., Zhou, Y., Zhao, J. et al. Video-based person re-identification by semi-supervised adaptive stepwise learning. Pattern Anal Applic 24, 1769–1776 (2021). https://doi.org/10.1007/s10044-021-01016-5

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