Real-time object tracking using color-based probability matching

The motivation of the proposed method is to solve typical problem for multiple object tracking like partial background change, motion blur, object's occlusion, temporal movement, merging of object(s), etc. In this paper, we have proposed a color-based probability matching for real-time object(s) tracking. The proposed method is capable to detect moving object(s) and track the same object(s) which appear in the subsequent frame. Initially, object detection is carried by using two different approaches (i.e. Background Subtraction Modeling and Optical Flow Method) and pros and cons of both methods are discussed. Then, color-based probability is calculated for an individually detected object(s) in ensuing frames.