MACHINE PERCEPTION AND ROBOTICS GROUP

Dept. of Robotics Science and Technology,
Chubu University

We present a method for estimating activities of multiple, interacting objects detected by a video surveillance system. The activities are described in a stochastic context because our method is concerned with humans and uses noisy features detected from video. To monitor activities in this context, we introduce the concept of an attribute set for each blob, consisting of object type, action, and interaction. Using probabilistic relations introduced by a specific Markov model of these attribute sets, the activity descriptions are estimated from surveillance video.