Real-time human activity recognition using external and internal spatial features

Htike@Muhammad Yusof, Zaw Zaw and Egerton, Simon and Kuang, Ye Chow (2010) Real-time human activity recognition using external and internal spatial features. In: Sixth International Conference on Intelligent Environments (IE), 19-21 July 2010, Kuala Lumpur, Malaysia.

PDF Restricted to Repository staff only Download (958kB) | Request a copy

Abstract

Human activity recognition has become very popular in the field of computer vision. In this paper, we present a simple, robust and computationally efficient algorithm, architecture and implementation to recognise and classify human activities in real-time using very few training data. We employ a spatio-temporal representation of human activities by combining trajectory information and invariant spatial information of the subjects. Activities are classified by a support vector machine (SVM) with a radial basis kernel. Optimal parameters for the SVM are found through a 10-fold cross-validation. Experimental results demonstrate that the proposed system is effective and efficient. When tested on the Weizmann dataset, the system achieves a recognition rate above 90% for one-shot learning which is above benchmark scores in accordance with the literature. The system is also found to be robust against noise, deformation and variation in viewpoints. The system is feasible to operate efficiently in real-time and deployable in intelligent environments.

Item Type:	Conference or Workshop Item (Full Paper)
Additional Information:	6919/ 43202. 978-0-7695-4149-5/10 ©2010 IEEE DOI 10.1109/IE.2010.1
Uncontrolled Keywords:	human activity recognition; real-time, artificial intelligence, computer vision
Subjects:	A General Works > AI Indexes (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering
Depositing User:	Mr. Zaw Zaw Htike
Date Deposited:	08 Jun 2015 11:01
Last Modified:	08 Jun 2015 11:34
URI:	http://irep.iium.edu.my/id/eprint/43202

Actions (login required)

View Item