| Citation: | SUN Yibo, ZHANG Wenjing, WANG Rong, et al. Pedestrian re-identification method based on channel attention mechanism[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(5): 881-889. doi: 10.13700/j.bh.1001-5965.2020.0684(in Chinese)
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To address the problem of insufficient expression of pedestrian characteristics, we propose a pedestrian re-identification method based on channel attention mechanism. The channel attention mechanism named SE module is embedded in the backbone network ResNet50 to weight and strengthen the key feature information. The dynamic activation function is used to dynamically adjust the parameters of ReLU according to the input characteristics, and enhance the nonlinear expression ability of the network model. The gradient centralization algorithm is introduced into the Adam optimizer to improve the training speed and generalization ability of the network model. Experiments on the three mainstream datasets: Market1501, DukeMTMC-ReID and CUHK03 show that Rank-1 is increased by 2.17%, 2.38%, and 3.50% respectively, and mAP is increased by 3.07%, 3.39%, and 4.14% respectively. The results indicate that our approach can extract more robust pedestrian expression features and achieve higher recognition accuracy.
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