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DSF-net: occluded person re-identification based on dual structure features

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Abstract

In many person re-identification application scenarios, such as supermarkets, subway stations, and streets, it is necessary to solve an occlusion problem. We propose a dual branch named Dual structural Features Network to solve the problem. Our method obtains features embedding from two types of structural data, that is, Euclidean structural data and non-Euclidean structural data. We argue that the features of these two structures are equally important to ease the occlusion problem. In our first branch, we introduce a Position Attention Drop Block to extract the Euclidean structural feature. This branch focuses on the information that pixels can represent within a certain receptive field. In order to better target our network at the occlusion problem, we propose a drop method based on the pixel attention score of the person image, in which the area with the highest score is lost. In this way, we design a network that pays more attention to other more detailed information. In the other branch, a new U-shaped Residual Graph Convolutional Network is used to extract the features from non-Euclidean structural data, which is an effective multilayer graph convolution. We argue that good non-Euclidean structural data can express more topological correlation information, thereby reducing the interference of the occluded part. From the experimental results, we have achieved competitive performance with state-of-the-art methods, and our method is especially effective for person re-identification with occluded body parts.

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Data availability

The datasets used in this paper are public datasets. The data that support the fundings of the study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work is partly supported by The National Natural Science Foundation of China (No. 61876158) and the Fundamental Research Funds for the Central Universities (2682021ZTPY030).

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Authors and Affiliations

  1. Tangshan Institute, Southwest Jiaotong University, Tangshan, 063000, Hebei, China

    Yueqiao Fan, Xun Gong & Yuning He

  2. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 610031, Sichuan, China

    Xun Gong

  3. Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province, Chengdu, 610031, Sichuan, China

    Xun Gong

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  1. Yueqiao Fan
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Correspondence to Xun Gong.

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The research described in the article was performed with funding from The National Natural Science Foundation of China. Dr. Gong is a principal of the Foundation. However, none of these foundation is described or presented in this article. The foundation is used to promote the development of basic research in the natural sciences. The Institute receives research grants from The National Natural Science Foundation of China. However, there are no other conflicts of interest.

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Fan, Y., Gong, X. & He, Y. DSF-net: occluded person re-identification based on dual structure features. Neural Comput & Applic 35, 3537–3550 (2023). https://doi.org/10.1007/s00521-022-07927-6

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