In this paper, we propose a scheme to combine trajectory-based detection and body-based estimation to analyze human behavior in video scene. Our scheme is applied for a fast and automatic detection of pick-up/drop-off events within surveillance videos in indoor areas. A moving person is tracked globally using the mean-shift algorithm and modeled locally using an axis skeleton in a monocular video sequence. We detect and rectify the stationary pick-up/drop-off objects. The spatial-temporal relationship between object and person is measured and exploited to detect a pick-up/drop-off event. Our experimental results accu rately estimate the human-motion trajectory and infer the posture. The system operates at real-time speed (around 20 frames/second).
|Title of host publication||Proceedings of the 28th Symposium on Information Theory in the Benelux, May 24-25, 2007, Enschede, the Netherlands|
|Place of Publication||Enschede|
|Publisher||Werkgemeenschap voor Informatie- en Communicatietheorie (WIC)|
|Publication status||Published - 2009|