Moving object detection and tracking based on background subtraction

An approach to detect and track moving objects with a stationary camera is presented in this paper. The mixture Gaussian model is used as an adaptive background updating method. Based on subtraction foreground is separated from background, and then foreground objects are segmented with a modified binary connected component analysis. Kalman filtering is used in object tracking. To deal with problems caused by occlusions between objects in tracking, six representative categories are introduced and analyzed. Experiments on several outdoors video streams resulted with convictive object detection and tracking performance demonstrate its strong adaptability to lighting changes, shadows and occlusions.