Two-Stream Gait Network for Cross-View Gait Recognition

被引：0
|
作者：
Wang K. [1 ]
Lei Y. [1 ]
Zhang J. [1 ]
机构：
[1] Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2020年 / 33卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Computer Vision; Deep Learning; Gait Recognition; Two-Stream Gait Network;
D O I：
10.16451/j.cnki.issn1003-6059.202005001
中图分类号：
学科分类号：
摘要：
With data augmentation and network feature map augmentation, a two-stream gait network is proposed to enhance the robustness of the model against the influence of belongings and clothing variations. Firstly,both global features and local discriminative information in gait videos are extracted by two-stream network. Then, the representation of gait feature is obtained by integrating outputs of two streams. The proposed restricted random mask is utilized to promote the network to learn more discriminative features and reduce the influence of belongings and clothing variations simultaneously. Furthermore, a triplet loss sampling algorithm is improved to accelerate the training convergence speed of the network model. Experiments on datasets, namely CASIA-B and OU-MVLP, indicate that the proposed method achieves a high gait recognition accuracy under different bagging and clothing walking conditions. © 2020, Science Press. All right reserved.
引用
收藏
页码：383 / 392
页数：9
相关论文
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