Automatic video-based human motion analyzer for consumer surveillance system

W. Lao, Jungong Han, P.H.N. With, de

Research output: Contribution to journalArticleAcademicpeer-review

109 Citations (Scopus)


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).
Original languageEnglish
Pages (from-to)591-598
JournalIEEE Transactions on Consumer Electronics
Issue number2
Publication statusPublished - 2009


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