Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9173
Title: Human action segmentation and recognition via motion and shape analysis
Authors: Shao, L
Ji, L
Liu, Y 
Zhang, J
Issue Date: 2012
Source: Pattern recognition letters, 2012, v. 33, no. 4, p. 438-445
Abstract: In this paper, we present an automated video analysis system which addresses segmentation and detection of human actions in an indoor environment, such as a gym. The system aims at segmenting different movements from the input video and recognizing the action types simultaneously. Two action segmentation techniques, namely color intensity based and motion based, are proposed. Both methods can efficiently segment periodic human movements into temporal cycles. We also apply a novel approach for human action recognition by describing human actions using motion and shape features. The descriptor contains both the local shape and its spatial layout information, therefore is more effective for action modeling and is suitable for detecting and recognizing a variety of actions. Experimental results show that the proposed action segmentation and detection algorithms are highly effective.
Keywords: Human action recognition
Human action segmentation
Motion analysis
Motion history image
PCOG
Publisher: Elsevier
Journal: Pattern recognition letters 
ISSN: 0167-8655
EISSN: 1872-7344
DOI: 10.1016/j.patrec.2011.05.015
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