多尺度多特征融合的行人重识别模型
A Person Re-Identification Method with Multi-Scale and Multi-Feature Fusion
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摘要: 为了解决行人重识别中行人特征表达不充分、忽视相邻区域的语义相关性等问题,提出了多尺度多特征融合的行人重识别模型.首先,通过多分支结构获取包含不同信息的特征图;其次,通过组合相邻的局部区域,强调局部特征之间的语义相关性;最后,结合最大池化和平均池化的优势,从不同的方向学习更加全面的特征信息.分别在Market-1501, DukeMTMC-reID以及MSMT17数据集上进行实验,结果表明,在光照不同、拍摄角度不同等环境下,文中模型的mAP分别达到了88.40%, 78.50%, 59.20%,能够有效地提取行人特征,识别精度较高.Abstract: In order to solve the problem of insufficient expression of pedestrian features and ignoring the semantic relevance of adjacent areas in person re-identification, a multi-scale and multi-feature fusion person re-identification model is proposed. Firstly, feature maps containing different information are obtained by multi-branch structure. Secondly, the semantic correlation between local features is emphasized by combining adjacent local regions. Finally, combining the advantages of maximum pooling and average pooling,the model can learn more comprehensive feature information from different directions. Experiments are carried out on Market-1501, DukeMTMC-reID and MSMT17 respectively, it can be evaluated that the model can effectively extract pedestrian feature and has high recognition accuracy under different lighting and camera angles.
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