Chen Hao, Zhao Yan, Wang Shigang
College of Communication Engineering, Jilin University, Changchun 130012, China.
Sensors (Basel). 2023 Jan 10;23(2):774. doi: 10.3390/s23020774.
Person re-identification (Re-ID) plays an important role in the search for missing people and the tracking of suspects. Person re-identification based on deep learning has made great progress in recent years, and the application of the pedestrian contour feature has also received attention. In the study, we found that pedestrian contour feature is not enough in the representation of CNN. On this basis, in order to improve the recognition performance of Re-ID network, we propose a contour information extraction module (CIEM) and a contour information embedding method, so that the network can focus on more contour information. Our method is competitive in experimental data; the mAP of the dataset Market1501 reached 83.8% and Rank-1 reached 95.1%. The mAP of the DukeMTMC-reID dataset reached 73.5% and Rank-1 reached 86.8%. The experimental results show that adding contour information to the network can improve the recognition rate, and good contour features play an important role in Re-ID research.
行人重识别（Re-ID）在寻找失踪人员和追踪嫌疑人方面发挥着重要作用。近年来，基于深度学习的行人重识别取得了很大进展，行人轮廓特征的应用也受到了关注。在研究中，我们发现行人轮廓特征在卷积神经网络（CNN）的表示中不够充分。在此基础上，为了提高Re-ID网络的识别性能，我们提出了一种轮廓信息提取模块（CIEM）和一种轮廓信息嵌入方法，使网络能够关注更多的轮廓信息。我们的方法在实验数据中具有竞争力；数据集Market1501的平均精度均值（mAP）达到83.8%，排名第一（Rank-1）达到95.1%。DukeMTMC-reID数据集的mAP达到73.5%，Rank-1达到86.8%。实验结果表明，在网络中添加轮廓信息可以提高识别率，良好的轮廓特征在Re-ID研究中起着重要作用。
