Real-time human activity recognition using external and internal spatial features
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 sp...
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iium-432022015-06-08T03:34:55Z http://irep.iium.edu.my/43202/ Real-time human activity recognition using external and internal spatial features Htike@Muhammad Yusof, Zaw Zaw Egerton, Simon Kuang, Ye Chow AI Indexes (General) 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. 2010-07-19 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/43202/1/IE_2010_-_1.PDF 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. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5673973 |
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English |
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AI Indexes (General) |
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AI Indexes (General) Htike@Muhammad Yusof, Zaw Zaw Egerton, Simon Kuang, Ye Chow Real-time human activity recognition using external and internal spatial features |
description |
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. |
format |
Conference or Workshop Item |
author |
Htike@Muhammad Yusof, Zaw Zaw Egerton, Simon Kuang, Ye Chow |
author_facet |
Htike@Muhammad Yusof, Zaw Zaw Egerton, Simon Kuang, Ye Chow |
author_sort |
Htike@Muhammad Yusof, Zaw Zaw |
title |
Real-time human activity recognition using external and internal spatial features |
title_short |
Real-time human activity recognition using external and internal spatial features |
title_full |
Real-time human activity recognition using external and internal spatial features |
title_fullStr |
Real-time human activity recognition using external and internal spatial features |
title_full_unstemmed |
Real-time human activity recognition using external and internal spatial features |
title_sort |
real-time human activity recognition using external and internal spatial features |
publishDate |
2010 |
url |
http://irep.iium.edu.my/43202/ http://irep.iium.edu.my/43202/ http://irep.iium.edu.my/43202/1/IE_2010_-_1.PDF |
first_indexed |
2023-09-18T21:01:34Z |
last_indexed |
2023-09-18T21:01:34Z |
_version_ |
1777410658194685952 |