文章基本信息

标题：Person Re-Identification Based on Attention Mechanism and Context Information Fusion
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作者：Shengbo Chen ; Hongchang Zhang ; Zhou Lei 等
期刊名称：Future Internet
电子版ISSN：1999-5903
出版年度：2021
卷号：13
期号：3
页码：72
DOI：10.3390/fi13030072
出版社：MDPI Publishing
摘要：Person re-identification (ReID) plays a significant role in video surveillance analysis. In the real world, due to illumination, occlusion, and deformation, pedestrian features extraction is the key to person ReID. Considering the shortcomings of existing methods in pedestrian features extraction, a method based on attention mechanism and context information fusion is proposed. A lightweight attention module is introduced into ResNet50 backbone network equipped with a small number of network parameters, which enhance the significant characteristics of person and suppress irrelevant information. Aiming at the problem of person context information loss due to the over depth of the network, a context information fusion module is designed to sample the shallow feature map of pedestrians and cascade with the high-level feature map. In order to improve the robustness, the model is trained by combining the loss of margin sample mining with the loss function of cross entropy. Experiments are carried out on datasets Market1501 and DukeMTMC-reID, our method achieves rank-1 accuracy of 95.9% on the Market1501 dataset, and 90.1% on the DukeMTMC-reID dataset, outperforming the current mainstream method in case of only using global feature.
关键词：deep learning; person re-identification; attention mechanism; context information fusion; margin sample mining deep learning ; person re-identification ; attention mechanism ; context information fusion ; margin sample mining