Motion-Based Video Object Tracking in the Compressed Domain

In this paper an algorithm for real-time unsupervised segmentation and tracking of a moving object is proposed. This is performed within the compressed domain using motion information only. Initial object segmentation is done using iterative rejection, taking advantage of its computational efficiency. The system seeks to overcome its disadvantages, namely a delay in object macroblocks appearing after consistency checking and non-identification of macroblocks containing object boundaries, by taking a model based approach to object tracking. The output of iterative rejection is used to update the model after tracking has taken place in each frame. Experimental results on a number of MPEG-2 encoded sequences demonstrate its effectiveness in identifying and tracking an object of interest from a compressed video stream and that the system is better than purely using iterative rejection as a segmentation method.