逐点特征匹配的跨域行人重识别方法

doi:10.16451/j.cnki.issn1003-6059.202206004

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Abstract To improve the poor generalization and cross-domain capability of the existing direct cross-dataset person re-identification methods, a cross-domain person re-identification method based on point-by-point feature matching is proposed. By the proposed method, the model only needs to be trained on the source domain and tested on the target domain to achieve better results. Firstly, to improve the poor robustness of the network for style and color of cross-domain pedestrian images, instance normalization layer(IN) is introduced into the ResNet50 basic network to extract image features. Secondly, the multi-head self-attention module of Transformer is combined with convolution to enhance the representation ability of features. Finally, by establishing a point-by-point feature mapping relationship in the deep features, image matching is regarded as a point-by-point process of finding the local optimum to improve the ability of the proposed model to resist perspective changes in unknown scenes and enhance its generalization. The experimental results show that the advantages of the proposed method in improving the generalization ability.

Key words： Person Re-identification Cross-Domain Person Re-identification Instance Normalization Multi-head Self-Attention Point-by-Point Matching

Received: 25 February 2022

ZTFLH:

TP 391

Fund:National Natural Science Foundation of China(No.61871278), Natural Science Foundation of Sichuan Province(No.2022NSFSC0922), Sichuan Science and Technology Plan Project(No.2021YFS0239)

Corresponding Authors: HE Xiaohai, Ph.D., professor. His research interests include image processing, pattern recognition, computer vision and image compression.

About author:: YANG Ping, master student. Her research interests include deep learning and person re-identification.
WU Xiaohong, Ph.D., associate profe-ssor. Her research interests include image processing, pattern recognition and computer vision.
CHEN Honggang, Ph.D., associate professor. His research interests include image/video processing, computer vision and artificial intelligence.
LIU Qiang, Ph.D. candidate. His research interests include image processing, person re-identification and computer vision.
LI Bo, master student. His research inte-rests include computer vision and pattern re-cognition.

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Cite this article:

YANG Ping,WU Xiaohong,HE Xiaohai等. Cross-Domain Person Re-identification Method Based on Point-by-Point Feature Matching[J]. Pattern Recognition and Artificial Intelligence, 2022, 35(6): 516-525.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202206004 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2022/V35/I6/516

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