Model-based viewpoint invariant human activity recognition from uncalibrated monocular video sequence

There is growing interest in human activity recognition systems, motivated by their numerous promising applications in many domains. Despite much progress, most researchers have narrowed the problem towards fixed camera viewpoint owing to inherent difficulty to train their systems across all possibl...

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Main Authors:	Htike@Muhammad Yusof, Zaw Zaw, Egerton, Simon, Kuang, Ye Chow
Format:	Article
Language:	English English
Published:	Springer-Verlag, Berlin, Germany 2010
Subjects:	AI Indexes (General)
Online Access:	http://irep.iium.edu.my/43203/ http://irep.iium.edu.my/43203/ http://irep.iium.edu.my/43203/4/43203_Model_based_viewpoint.pdf http://irep.iium.edu.my/43203/5/43203_Model_based_viewpoint_CoverTOC.pdf

id	iium-43203
recordtype	eprints
spelling	iium-432032015-06-05T04:17:04Z http://irep.iium.edu.my/43203/ Model-based viewpoint invariant human activity recognition from uncalibrated monocular video sequence Htike@Muhammad Yusof, Zaw Zaw Egerton, Simon Kuang, Ye Chow AI Indexes (General) There is growing interest in human activity recognition systems, motivated by their numerous promising applications in many domains. Despite much progress, most researchers have narrowed the problem towards fixed camera viewpoint owing to inherent difficulty to train their systems across all possible viewpoints. Fixed viewpoint systems are impractical in real scenarios. Therefore, we attempt to relax the fixed viewpoint assumption and present a novel and simple framework to recognize and classify human activities from uncalibrated monocular video source from any viewpoint. The proposed framework comprises two stages: 3D human pose estimation and human activity recognition. In the pose estimation stage, we estimate 3D human pose by a simple search-based and tracking-based technique. In the activity recognition stage, we use Nearest Neighbor, with Dynamic Time Warping as a distance measure, to classify multivariate time series which emanate from streams of pose vectors from multiple video frames. We have performed some experiments to evaluate the accuracy of the two stages separately. The encouraging experimental results demonstrate the effectiveness of our framework. Springer-Verlag, Berlin, Germany 2010 Article PeerReviewed application/pdf en http://irep.iium.edu.my/43203/4/43203_Model_based_viewpoint.pdf application/pdf en http://irep.iium.edu.my/43203/5/43203_Model_based_viewpoint_CoverTOC.pdf Htike@Muhammad Yusof, Zaw Zaw and Egerton, Simon and Kuang, Ye Chow (2010) Model-based viewpoint invariant human activity recognition from uncalibrated monocular video sequence. Lecture Notes in Computer Science (LNCS), 6464. 142-152 . ISSN 0302-9743 (P), 1611-3349 (O) http://link.springer.com/chapter/10.1007%2F978-3-642-17432-2_15
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	AI Indexes (General)
spellingShingle	AI Indexes (General) Htike@Muhammad Yusof, Zaw Zaw Egerton, Simon Kuang, Ye Chow Model-based viewpoint invariant human activity recognition from uncalibrated monocular video sequence
description	There is growing interest in human activity recognition systems, motivated by their numerous promising applications in many domains. Despite much progress, most researchers have narrowed the problem towards fixed camera viewpoint owing to inherent difficulty to train their systems across all possible viewpoints. Fixed viewpoint systems are impractical in real scenarios. Therefore, we attempt to relax the fixed viewpoint assumption and present a novel and simple framework to recognize and classify human activities from uncalibrated monocular video source from any viewpoint. The proposed framework comprises two stages: 3D human pose estimation and human activity recognition. In the pose estimation stage, we estimate 3D human pose by a simple search-based and tracking-based technique. In the activity recognition stage, we use Nearest Neighbor, with Dynamic Time Warping as a distance measure, to classify multivariate time series which emanate from streams of pose vectors from multiple video frames. We have performed some experiments to evaluate the accuracy of the two stages separately. The encouraging experimental results demonstrate the effectiveness of our framework.
format	Article
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	Model-based viewpoint invariant human activity recognition from uncalibrated monocular video sequence
title_short	Model-based viewpoint invariant human activity recognition from uncalibrated monocular video sequence
title_full	Model-based viewpoint invariant human activity recognition from uncalibrated monocular video sequence
title_fullStr	Model-based viewpoint invariant human activity recognition from uncalibrated monocular video sequence
title_full_unstemmed	Model-based viewpoint invariant human activity recognition from uncalibrated monocular video sequence
title_sort	model-based viewpoint invariant human activity recognition from uncalibrated monocular video sequence
publisher	Springer-Verlag, Berlin, Germany
publishDate	2010
url	http://irep.iium.edu.my/43203/ http://irep.iium.edu.my/43203/ http://irep.iium.edu.my/43203/4/43203_Model_based_viewpoint.pdf http://irep.iium.edu.my/43203/5/43203_Model_based_viewpoint_CoverTOC.pdf
first_indexed	2023-09-18T21:01:34Z
last_indexed	2023-09-18T21:01:34Z
_version_	1777410658340438016

Model-based viewpoint invariant human activity recognition from uncalibrated monocular video sequence

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