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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Official URL: http://dx.doi.org/10.1016/j.patrec.2011.05.015

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
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Related URLs:
Depositing User: Paul Burns
Date Deposited: 10 Jun 2015 15:16
Last Modified: 03 Nov 2016 12:06
URI: http://nrl.northumbria.ac.uk/id/eprint/22845

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