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	MULTI-VIEW GAIT RECOGNITION WITH INCOMPLETE TRAINING DATA
	Wei, Lan ; Tian, Yonghong ; Wang, Yaowei ; Huang, Tiejun
	2014
关键词	Gait recognition View Transformation Model (VTM) View Feature Recovering Model (VFRM) Geodesic distance based K-Nearest Neighbor (GKNN) Incomplete data FRAMEWORK
英文摘要	Changes in the viewing angles pose a major challenge for gait recognition because the human gait silhouettes can be different under the various viewing angles. Recently, View Transformation Model (VTM) was proposed to tackle this problem by transforming gait features from across views to a common viewing angle. However, VTM must use the data of subjects crossing all views to train the pre-constructed model, which might be unsuitable for the real applications. To address this problem, this paper proposes a View Feature Recovering Model (VFRM) to generate the VTM with incomplete training data. In our algorithm, if the gait signature of a pedestrian is missing under a view, it can be recovered from the K-nearest pedestrians whose gait features are available in the same view. Moreover, the Geodesic distance based K-Nearest Neighbor (GKNN) algorithm is adopted in our algorithm to better measure the neighborhood between two pedestrians. Experimental results on a benchmark database has demonstrated the effectiveness of our method.; EI; CPCI-S(ISTP); yhtian@pku.edu.cn; Septmber; 2014-September
语种	英语
出处	2014 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME)
DOI标识	10.1109/ICME.2014.6890315
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/423835]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Wei, Lan,Tian, Yonghong,Wang, Yaowei,et al. MULTI-VIEW GAIT RECOGNITION WITH INCOMPLETE TRAINING DATA. 2014-01-01.

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