A New Spatio-Temporal Markov Random Fields for Video Object Tracking in the Compressed Domain

The compressed-domain video object tracking, are needed for a tracking framework with both reasonable accuracy and reasonable complexity still exists. This paper presents a method or tracking moving objects in H.264/AVC-compressed video sequences using a spatio-temporal Markov random field (STMRF) model. An ST-MRF model naturally integrates the spatial and temporal aspects of the object’s motion. Built upon such a model, recent advances in the field of global motion estimation enable outlier elimination in the background area, and thus a more precise definition of the foreground is achieved the proposed method works in the compressed domain and uses only the motion vectors (MVs) and block coding modes from the compressed bitstream to perform tracking. First, the MVs are preprocessed through intracoded block motion approximation and global motion compensation. At each frame, the decision of whether a particular block belongs to the object being tracked is made with the help of the ST-MRF model, which is updated from frame to frame in order to follow the changes in the object’s motion.