ACTION DETECTION: CONTENT MODELLING, DESCRIPTION AND RECOGNITION

Abdullah, L. N. and Mohd Noah, Shahrul Azman and Tengku Sembok, Tengku Mohd and Omar, Khairuddin (2007) ACTION DETECTION: CONTENT MODELLING, DESCRIPTION AND RECOGNITION. In: 2nd International Conference on Informatics (Informatics 2007), Hilton Petaling Jaya Hotel, Petaling Jaya, Selangor, Malaysia.

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Official URL: http://dspace.fsktm.um.edu.my/handle/1812/393

Abstract

In this paper, we directly modelled the hierarchy and shared structures of human behaviours, and we present a framework of the Hidden Markov model based application for the problem of activity recognition. It is crucial to exploit and to robustly model and recognize complex human activities. Our goal is to exploit human motion to perform action recognition. Towards the end, we introduced the use of the Hidden Markov model, a rich stochastic model that has been recently extended to handle structures, for representing and recognizing simple actions. We proposed a framework for recognizing actions by measuring image and action-based information from video with the following characteristics: feature extraction; the method deals with both visual and auditory information, and captures both spatial and temporal characteristics; and the extracted features are natural, in the sense that they are closely related to the human perceptual processing. Our effort was to implementing idea of action identification by extracting syntactic properties of a video such as edge feature extraction, colour distribution, audio and motion vectors. In this paper, we present a two layers hierarchical module for action recognition. The first one performs supervised learning to recognize individual actions of participants using low-level audio-visual (AV) features. The second layer models actions, using the output of the first layer as observations, and producing a temporal segmentation of an action into group action segments. Both layers use Hidden Markov model-based approaches for action recognition and clustering, respectively.

Item Type: Conference or Workshop Item (Paper)
Subjects: Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: MS SITI NUR ATIKAH MOHAMAD RUSDI
Date Deposited: 15 Jul 2013 07:23
Last Modified: 15 Jul 2013 07:23
URI: http://repository.um.edu.my/id/eprint/316

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