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Multi-cascaded attention and overlapping part features network for person re-identification

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Abstract

Person re-identification (Person re-ID) is a challenging task in the field of computer vision. Generally, re-ID is regarded as an image retrieval problem; this task aims at searching a query person across multiple non-overlapping cameras. Re-ID is an important research direction after facial recognition that has many application scenarios, such as intelligent security, intelligent homing system, and unmanned supermarket. In practice, the identification would be deteriorated by the pedestrian postures, occlusions, and lighting conditions. Cropped images of pedestrians are often obtained from automatic detectors; this type of detection introduces two types of errors: part missing and excessive background. In previous works, fine-grained information has been proved to be useful for pedestrian retrieval. In this paper, we observe that multiple attention blocks can perform long-range multi-hop communication. To tackle the problem of excessive background, multistage cascaded attention blocks are introduced to put more focus on the information about human body. On the other hand, the part-level features have been proven to be effective against high quality of feature representation, but considering traditional vertical segmentation brings part inconsistency and lost some detailed semantic clues; the concept of overlapping features has been proposed to overcome this problem. Experiments on two large-scale re-ID datasets show that our method improves the learned representation of the feature embeddings and achieved competitive results.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. U1833115).

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  1. College of Computer Science, Sichuan University, Chengdu, China

    Xin Zhang, Mingzheng Hou, Xiwen Deng & Ziliang Feng

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  1. Xin Zhang
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  2. Mingzheng Hou
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  3. Xiwen Deng
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  4. Ziliang Feng
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Correspondence to Xin Zhang.

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Zhang, X., Hou, M., Deng, X. et al. Multi-cascaded attention and overlapping part features network for person re-identification. SIViP 16, 1525–1532 (2022). https://doi.org/10.1007/s11760-021-02106-x

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