Tracking and counting people in visual surveillance systems

The greatest challenge on monitoring characters from a monocular video scene is to track targets under occlusion conditions. In this work, we present a scheme to automatically track and count people in a surveillance system. First, a dynamic background subtraction module is employed to model light variation and then to determine pedestrian objects from a static scene. To identify foreground objects as characters, positions and sizes of foreground regions are treated as decision features. Moreover, the performance to track individuals is improved by using the modified overlap tracker, which investigates the centroid distance between neighboring objects to help on target tracking in occlusion states of merging and splitting. On the experiments of tracking and counting people in three video sequences, the results exhibit that the proposed scheme can improve the averaged detection ratio about 10% as compared to the conventional work.