Human action segmentation and recognition via motion and shape analysis

Shao, Ling, Ji, Ling, Liu, Yan and Zhang, Jianguo (2012) Human action segmentation and recognition via motion and shape analysis. Pattern Recognition Letters, 33 (4). pp. 438-445. ISSN 0167-8655

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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.

Item Type: Article
Uncontrolled Keywords: human action segmentation,motion analysis,pcog,motion history image,human action recognition
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: Pure Connector
Date Deposited: 07 Feb 2017 02:39
Last Modified: 25 Jul 2018 13:15
URI: https://ueaeprints.uea.ac.uk/id/eprint/62331
DOI: 10.1016/j.patrec.2011.05.015

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