Zhou Jieqian, Zhao Shuai, Li Shengjie, Cheng Bo, Chen Junliang
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel). 2024 Aug 30;24(17):5638. doi: 10.3390/s24175638.
This research proposes constructing a network used for person re-identification called MGNACP (Multiple Granularity Network with Attention Mechanisms and Combination Poolings). Based on the MGN (Multiple Granularity Network) that combines global and local features and the characteristics of the MGN branch, the MGNA (Multiple Granularity Network with Attentions) is designed by adding a channel attention mechanism to each global and local branch of the MGN. The MGNA, with attention mechanisms, learns the most identifiable information about global and local features to improve the person re-identification accuracy. Based on the constructed MGNA, a single pooling used in each branch is replaced by combination pooling to form MGNACP. The combination pooling parameters are the proportions of max pooling and average pooling in combination pooling. Through experiments, suitable combination pooling parameters are found, the advantages of max pooling and average pooling are preserved and enhanced, and the disadvantages of both types of pooling are overcome, so that poolings can achieve optimal results in MGNACP and improve the person re-identification accuracy. In experiments on the Market-1501 dataset, MGNACP achieved competitive experimental results; the values of mAP and top-1 are 88.82% and 95.46%. The experimental results demonstrate that MGNACP is a competitive person re-identification network, and that the attention mechanisms and combination poolings can significantly improve the person re-identification accuracy.
本研究提出构建一个用于行人重识别的网络，称为MGNACP（具有注意力机制和组合池化的多粒度网络）。基于结合全局和局部特征的MGN（多粒度网络）以及MGN分支的特点，通过在MGN的每个全局和局部分支中添加通道注意力机制来设计MGNA（具有注意力的多粒度网络）。带有注意力机制的MGNA学习全局和局部特征中最具辨识度的信息，以提高行人重识别准确率。基于构建好的MGNA，将每个分支中使用的单个池化替换为组合池化，从而形成MGNACP。组合池化参数是组合池化中最大池化和平均池化的比例。通过实验，找到了合适的组合池化参数，保留并增强了最大池化和平均池化的优点，克服了这两种池化的缺点，使得池化在MGNACP中能够取得最优结果，并提高行人重识别准确率。在Market-1501数据集上的实验中，MGNACP取得了具有竞争力的实验结果；mAP和top-1的值分别为88.82%和95.46%。实验结果表明，MGNACP是一个具有竞争力的行人重识别网络，注意力机制和组合池化能够显著提高行人重识别准确率。
