| Citation: | WANG J，LI P T，ZHAO R F，et al. A person re-identification method for fusing convolutional attention and Transformer architecture[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（2）：466-476 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0456
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Person Re-identification technology is one of the important methods in intelligent security systems. In order to build a person re-identification model suitable for various complex scenarios, this article proposed a method of Fusing Convolutional Attention and Transformer architecture (FCAT) based on existing convolutional neural networks and Transformer models to enhance the Transformer’s attention to local detail information. This method mainly improves the transformer's ability to extract local detail features indirectly by embedding convolutional space attention and channel attention respectively to enhance the attention to important regions and important channel features in the image. Comparative ablation experiments on three publicly available pedestrian re-identification datasets demonstrate that the proposed method achieves comparable results on non-occluded datasets and significantly improves performance on occluded datasets. Additionally, the proposed model is more lightweight, leading to improved inference speed without increasing additional computational load or model parameters.
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