Human and vehicles tracking in surveillance videos

In our work describes a method for accurately tracking persons in indoor surveillance video stream obtained from a static camera with difficult scene properties including illumination changes and solves the major occlusion problem by using Kalman filtering. First, moving objects are precisely extracted by determining its motion, for further processing. The scene illumination changes are averaged to obtain the accurate moving object during background subtraction process. In case of objects occlusion, we use the color feature information to accurately distinguish between objects. The method is able to identify moving persons, track them and provide unique tag for the tracked persons. The effectiveness of the proposed method is demonstrated with experiments in an indoor environment.