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Learning part-alignment feature for person re-identification with spatial-temporal-based re-ranking method

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Abstract

Person re-identification is to identify a target person in different cameras with non-overlapping views. It is a challenging task due to various viewpoints of persons, diversified illuminations, and complicated environments. In addition, body parts are usually misaligned because of the less precise bounding boxes, which play a significant role in person re-identification, so it is crucial to make them aligned for better performance. In this paper, we propose a network to learn powerful features combining global features and local-alignment features for person re-identification. For each body part, instead of fixed horizontal partition, a key points detection network is adopted to locate body parts that contain more precise and distinctive information. Besides, a novel re-ranking approach is proposed to refine the rough initial rank list by exploiting the spatial-temporal information. Unlike most existing re-ranking based methods fine-tuning the rough initial rank list only by k-nearest neighbors and their k-reverse-nearest neighbors, our method exploits spatial-temporal information which can be easily stored in the name of images, so it can be implemented in any baseline to improve the performance. Experiments on the GRID, Market-1501, and DukeMTMC-reID are conducted to prove the effectiveness of our method.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No.61972030), the Fundamental Research Funds for Central Universities (No. 2018JBM017) and the Hebei Province Key Research and Development Projects(18210305D).

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Authors and Affiliations

  1. Department of Computer Science, Beijing Jiaotong University, No.3 Shangyuancun, Haidian District, Beijing, 100044, People’s Republic of China

    Zhongyi Li, Yi Jin, Yidong Li, Congyan Lang, Songhe Feng & Tao Wang

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  1. Zhongyi Li
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Correspondence to Yi Jin.

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Li, Z., Jin, Y., Li, Y. et al. Learning part-alignment feature for person re-identification with spatial-temporal-based re-ranking method. World Wide Web 23, 1907–1923 (2020). https://doi.org/10.1007/s11280-019-00734-5

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