Continuous tracking within and across camera streams

This paper presents a new approach for continuous tracking of moving objects observed by multiple, heterogeneous cameras. Our approach simultaneously processes video streams from stationary and pan-tilt-zoom cameras. The detection of moving objects from moving camera streams is performed by defining an adaptive background model that takes into account the camera motion approximated by an affine transformation. We address the tracking problem by separately modeling motion and appearance of the moving objects using two probabilistic models. For the appearance model, multiple color distribution components are proposed for ensuring a more detailed description of the object being tracked. The motion model is obtained using a Kalman filter (KF) process, which predicts the position of the moving object. The tracking is performed by the maximization of a joint probability model. The novelty of our approach consists in modeling the multiple trajectories observed by the moving and stationary cameras in the same KF framework. It allows deriving a more accurate motion measurement for objects simultaneously viewed by the two cameras and an automatic handling of occlusions, errors in the detection and camera handoff. We demonstrate the performances of the system on several video surveillance sequences.