标题:GAIT RECOGNITION BASED ON 3D SKELETON JOINTS CAPTURED BY KINECT
作者:Wang, Wei; Sun, Jiande; Li, Jing; Zhao, Dong
作者机构:[Sun, Jiande; Li, Jing] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan, Peoples R China.; [Wang, Wei; Zhao, Dong] Shandong Univ, Sch Informat 更多
会议名称:23rd IEEE International Conference on Image Processing (ICIP)
会议日期:SEP 25-28, 2016
来源:2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
出版年:2016
卷:2016-August
页码:3151-3155
DOI:10.1109/ICIP.2016.7532940
关键词:Terms Gait Recognition; Second Generation Kinect; View-Invariant; 3D; Skeleton Joint; Gait Dataset
摘要:2D-video-based gait recognition techniques have been studied for decades, but there are still many challenges, one of which is the robustness against the variation of view angle. In this paper. the second generation Kinect (Kinect V2) is used as a tool to establish a 3D-skeleton-based gait database, which includes both 3D information of the skeleton joints and the corresponding 2D silhouette images captured by Kinect V2. Based on this dataset, a human walking model is built, and the static and dynamic features are extracted, which are verified to be view-invariant for gait recognition. Referring to the walking model, the gait recognition abilities for the static and dynamic features are investigated respectively and a gait recognition scheme based on the matching-level-fusion of the static and dynamic features is proposed, in which the recognition is achieved by the nearest neighbor classification method. Experiments show that the proposed scheme has robust recognition performance against the variation of view angle.
收录类别:CPCI-S;EI;SCOPUS
Scopus被引频次:1
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006699842&doi=10.1109%2fICIP.2016.7532940&partnerID=40&md5=048bcdf3116e86e2b09df3968f295941
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