Tracking People in Crowded Scenes across Multiple Cameras

We present a novel approach for continuous detection and tracking of moving objects observed by multiple stationary cameras. We address the tracking problem by simultaneously modeling motion and appearance of the moving objects. The object’s appearance is represented using color distribution model invariant to 2D rigid and scale transformation. It provides an efficient blob similarity measure for tracking. The motion models are obtained using a Kalman Filter process, which predicts the position of the moving object in both 2D and 3D. The tracking is performed by the maximization of a joint probability model reflecting objects’ motion and appearance. The novelty of our approach consists in integrating multiple cues and multiple views in a Joint Probability Data Association Filter for tracking a large number of moving people with partial and total occlusions. We demonstrate the performance of the proposed method on a soccer game captured by two stationary cameras.