Tracking objects through occlusions using improved Kalman filter

In a visual surveillance system, robust tracking of moving objects which are partially or even fully occluded is very difficult. In this paper, we present a method of tracking objects through occlusions using a combination of Kalman filter and color histogram. By changing covariance of process noise and measurement noise in Kalman filter, this method can maintain the tracking of moving objects before, during, and after occlusion. Experiments which described on several test sequences of the open PETS2000 and PETS2001 datasets have demonstrated the effectiveness and robustness of this method.