TriHard Loss Based Multi-Task Person Re-identification

被引：0

作者：

Chen Q. ^{[1
]}

Chen Y. ^{[1
]}

机构：

[1] Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Jiangnan University, Wuxi

来源：

Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2019年 / 31卷 / 07期

关键词：

Multi-task network; Pedestrian attributes; Person re-identification; Triplet loss;

D O I：

10.3724/SP.J.1089.2019.17463

中图分类号：

学科分类号：

摘要：

Aiming to enhance features discrimination for person re-identification, a deep multi-task person re-identification network based on TriHard loss is proposed. By learning identity and attributes labels simultaneously, the network can achieve more discriminative information of pedestrians. Firstly, the pre-trained ResNet-50 is loaded to extract pedestrian features of pre-processed images. Secondly, pedestrian features are fed into the designed multi-task network which consists of two branches. The two branches are trained jointly by minimizing combined TriHard loss of identity and attribute. Finally, the trained model is used to extract pedestrian appearances and attributes features. The features are used for person re-identification and attributes recognition. From the experimental results on the Market-1501 dataset and the DukeMTMC-reID dataset, it shows that features extracted from the proposed network are more discriminative. The multitask network achieves higher identification accuracy over the state-of-the-arts, together with the person attributes recognition. © 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved.

引用

页码：1156 / 1165

页数：9

共 29 条

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