摘要： 针对行人重识别中由于姿势变化、视角改变、遮挡等引起的识别率不高的问题，提出了融合外观特征的行人重识别方法。该方法通过两个网络分支的设计，分别提取行人的全局特征和局部特征，二者融合后得到行人的外观特征。同时结合分类损失和度量学习损失，通过多任务学习策略对两个网络分支进行模型优化。此外，该模型设计了随机擦除算法，在数据集中加入噪声，增强模型的鲁棒性。实验结果表明：融合外观特征的行人重识别方法大大提高了行人重识别的准确率，在Market-1501数据集上rank1达到了92.82%、mAP 达到了80.51%，在DukeMTMC-reID数据集上rank1达到了85.06%、mAP达到了72.72%。

中图分类号： TP391
文献标识码： A
DOI： 10.19358/j.issn.2096-5133.2021.06.006
引用格式： 彭玉青，李伟，郭永芳. 融合外观特征的行人重识别方法[J].信息技术与网络安全，2021，40(6)：33-37，49.

Person re-identification incorporating appearance feature

Abstract： Aiming at the problem of low recognition rate caused by pose, viewpoint and occlusion in person re-identification, a method incorporating appearance feature is proposed. The method designs two network branches to extract global feature and local feature of pedestrians respectively, and the two are fused to obtain the appearance features of pedestrians. Simultaneously the model is optimized by a multi-task learning strategy for both network branches through combining classification loss and metric learning loss. In addition, the model combines with random erasing algorithm to add noise to the dataset for enhancing the robustness of the model. The experimental results show that the proposed method incorporating appearance feature greatly improves the accuracy of person re-ID, with rank-1 reaching 92.82% and mAP reaching 80.51% on the Market1501 dataset, and rank-1 reaching 85.06% and mAP reaching 72.72% on the DukeMTMC-reID dataset.

Key words : person re-identification；feature incorporating；random erasing；multi-task learning