Abstract 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.
Fund:Supported by National Natural Science Foundation of China(No.61673118), Shanghai Municipal Science and Technology Major Project(No.2018SHZDZX01)
About author:: (WANG Kun, master student. His research interests include machine learning, computer vision and gait recognition.);(LEI Yiming, Ph.D. candidate. His research interests include machine learning, computer vision and medical image proce-ssing.);(ZHANG Junping(Corresponding author), Ph.D., professor. His research interests include machine learning, intelligent transportation system and image processing.)
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2020, Vol. 33 
