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Proceedings Paper

Multi-module human motion analysis from a monocular video
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Paper Abstract

In this paper, we propose an effective framework for semantic analysis of human motion from a monocular video. As it is difficult to find a good motion description for humans, we focus on a reliable recognition of the motion type and estimate the body orientation involved in the video sequence. Our framework analyzes the body motion in three modules: a pre-processing module, matching module and semantic module. The proposed framework includes novel object-level processing algorithms, such as a local descriptor and a global descriptor to detect body parts and analyze the shape of the whole body as well. Both descriptors jointly contribute to the matching process by incorporating them into a new weighted linear combination for matching. We also introduce a simple cost function based on time-index di.erences to distinguish motion types and cycles in human motions. Our system can provide three different types of analysis results: (1) foreground person detection; (2) motion recognition in the sequence; (3) 3-D modeling of human motion based on generic human models. The proposed framework was evaluated and proved its effectiveness as it achieves the motion recognition and body-orientation classification at the accuracy of 95% and 98%, respectively.

Paper Details

Date Published: 29 January 2007
PDF: 9 pages
Proc. SPIE 6506, Multimedia Content Access: Algorithms and Systems, 65060M (29 January 2007); doi: 10.1117/12.705945
Show Author Affiliations
Weilun Lao, Eindhoven Univ. of Technology (Netherlands)
Jungong Han, Eindhoven Univ. of Technology (Netherlands)
Peter H. N. de With, Eindhoven Univ. of Technology (Netherlands)
LogicaCMG (Netherlands)

Published in SPIE Proceedings Vol. 6506:
Multimedia Content Access: Algorithms and Systems
Alan Hanjalic; Raimondo Schettini; Nicu Sebe, Editor(s)

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