|Author||Song, Chunfeng1; Huang, Yongzhen1; Huang, Yan1; Jia, Ning2; Wang, Liang1|
|Keyword||Gait recognition Video-based human identification End-to-end CNN Joint learning|
Gait recognition is one of the most important techniques for human identification at a distance. Most current gait recognition frameworks consist of several separate steps: silhouette segmentation, feature extraction, feature learning, and similarity measurement. These modules are mutually independent with each part fixed, resulting in a suboptimal performance in challenging conditions. In this paper, we integrate those steps into one framework, i.e., an end-to-end network for gait recognition, named GaitNet. It is composed of two convolutional neural networks: one corresponds to gait segmentation, and the other corresponds to classification. The two networks are modeled in one joint learning procedure which can be trained jointly. This strategy greatly simplifies the traditional step-by-step manner and is thus much more efficient for practical applications. Moreover, joint learning can automatically adjust each part to fit the global optimal objective, leading to obvious performance improvement over separate learning. We evaluate our method on three large scale gait datasets, including CASIA-B, SZU RGB-D Gait and a newly built database with complex dynamic outdoor backgrounds. Extensive experimental results show that the proposed method is effective and achieves the state-of-the-art results. The code and data will be released upon request. (C) 2019 Elsevier Ltd. All rights reserved.
|Funding Project||National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Capital Science and Technology Leading Talent Training Project[Z181100006318030] ; Beijing Science and Technology Project[Z181100008918010] ; National Key Research and Development Program of China[2016YEB1001000] ; CAS-AIR ; NVIDIA ; NVIDIA DGX-1 Al Supercomputer|
|WOS Keyword||RECOGNITION ; REPRESENTATION ; IMAGE ; MODEL ; FRAMEWORK ; QUALITY|
|WOS Research Area||Computer Science ; Engineering|
|Publisher||ELSEVIER SCI LTD|
|Corresponding Author||Wang, Liang|
|Affiliation||1.Chinese Acad Sci, Univ Chinese Acad Sci, Natl Lab Pattern Recognit, Ctr Res Intelligent Percept & Comp,Inst Automat, Beijing 100190, Peoples R China|
2.Univ Durham, Durham DH1 3LE, England
|Song, Chunfeng,Huang, Yongzhen,Huang, Yan,et al. GaitNet: An end-to-end network for gait based human identification[J]. PATTERN RECOGNITION,2019,96(106988):11.|
|APA||Song, Chunfeng,Huang, Yongzhen,Huang, Yan,Jia, Ning,&Wang, Liang.(2019).GaitNet: An end-to-end network for gait based human identification.PATTERN RECOGNITION,96(106988),11.|
|MLA||Song, Chunfeng,et al."GaitNet: An end-to-end network for gait based human identification".PATTERN RECOGNITION 96.106988(2019):11.|
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