Automatic video-based human motion analyzer for consumer surveillance system

With the continuous improvements in video-analysis techniques, automatic low-cost video surveillance gradually emerges for consumer applications. Video surveillance can contribute to the safety of people in the home and ease control of home-entrance and equipment-usage functions. In this paper, we s...

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Bibliographic Details
Published in:IEEE transactions on consumer electronics 2009-05, Vol.55 (2), p.591-598
Main Authors: Weilun Lao, Jungong Han, De With, P.H.N.
Format: Article
Language:English
Subjects:
Automatic control
calibration
Cameras
Classification
content analysis
Continuous improvement
Domestic safety
Electronics
Human behavior analysis
Humans
Layout
Motion analysis
Posture
realtime
Reconstruction
Safety devices
semantic
Semantics
Surveillance
surveillance video
tracking
Trajectories
Video sequences
Video surveillance
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Summary:With the continuous improvements in video-analysis techniques, automatic low-cost video surveillance gradually emerges for consumer applications. Video surveillance can contribute to the safety of people in the home and ease control of home-entrance and equipment-usage functions. In this paper, we study a flexible framework for semantic analysis of human behavior from a monocular surveillance video, captured by a consumer camera. Successful trajectory estimation and human-body modeling facilitate the semantic analysis of human activities and events in video sequences. An additional contribution is the introduction of a 3-D reconstruction scheme for scene understanding, so that the actions of persons can be analyzed from different views. The total framework consists of four processing levels: (1) a preprocessing level including background modeling and multiple-person detection, (2) an object-based level performing trajectory estimation and posture classification, (3) an event-based level for semantic analysis, and (4) a visualization level including camera calibration and 3-D scene reconstruction. Our proposed framework was evaluated and has shown its good quality (86% accuracy of posture classification and 90% for events) and effectiveness, as it achieves a near real-time performance (6-8 frames/second).
ISSN:0098-3063
1558-4127
DOI:10.1109/TCE.2009.5174427