Object segmentation using Graph Cuts for the H.264 compressed video with moving background

This paper proposed a novel approach to segment objects from the H.264 compressed video with moving background. At first, the noisy motion vectors are eliminated from the motion field by vector median filtering. Then the predicted motion field reconstructed by backward estimation is used to accumulate the motion field, which is followed by global motion compensation. After that, the hypothesis testing is used for initial region classification. Finally, the graph cuts technique is applied to partition objects by minimizing the energy function formulated by the model of Markov Random Field. The experimental results demonstrate efficient performance and good segmentation quality of the proposed method.