北京航空航天大学学报 ›› 2022, Vol. 48 ›› Issue (5): 881-889.doi: 10.13700/j.bh.1001-5965.2020.0684

• 论文 • 上一篇    下一篇

基于通道注意力机制的行人重识别方法

孙义博, 张文靖, 王蓉, 李冲, 张琪   

  1. 中国人民公安大学 信息网络安全学院, 北京 100038
  • 收稿日期:2020-12-08 发布日期:2022-05-30
  • 通讯作者: 李冲 E-mail:lichong7564@163.com
  • 基金资助:
    国家自然科学基金(62076246);中央高校基本科研业务费专项资金(2019JKF426)

Pedestrian re-identification method based on channel attention mechanism

SUN Yibo, ZHANG Wenjing, WANG Rong, LI Chong, ZHANG Qi   

  1. School of Information and Cyber Security, People’s Public Security University of China, Beijing 100038, China
  • Received:2020-12-08 Published:2022-05-30
  • Supported by:
    National Natural Science Foundation of China (62076246); the Fundamental Research Funds for the Central Universities (2019JKF426)

摘要: 针对行人特征表达不充分的问题,提出了一种基于通道注意力机制的行人重识别方法。将通道注意力机制SE模块嵌入到骨干网络ResNet50中,对关键特征信息进行加权强化;采用动态激活函数,根据输入特征动态调整ReLU的参数,增强网络模型的非线性表达能力;将梯度中心化算法引入Adam优化器,提升网络模型的训练速度和泛化能力。在Market1501、DukeMTMC-ReID和CUHK03主流数据集上对改进后的模型进行测试评价,Rank-1分别提升2.17%、2.38%和3.50%,mAP分别提升3.07%、3.39%和4.14%。结果表明:改进后的模型能够提取更强鲁棒性的行人表达特征,达到更高的识别精度。

关键词: 通道注意力机制, 动态激活函数, 梯度中心化, 特征提取, 行人重识别

Abstract: 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.

Key words: channel attention mechanism, dynamic activation function, gradient centralization, feature extraction, pedestrian re-identification

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