Moving Objects Segmentation in Video Sequence Based on Bayesian Network

This paper proposes an improvement over moving objects segmentation method for video sequence based on Bayesian network. The method integrates temporal and spatial features by Bayesian network through three fields, which are motion vector field, intensity segmentation field and object video segmentation field. Markov random field aims to push the spatial connectivity between regions. The improvement concentrates on the MAP estimation procedure in order to obtain the exact segmentation results. The Iterative MAP Estimation may cause much more error in estimation procedure and degrade the convergence of the algorithm. This paper proposes a non-iterative Estimation as an improvement for this algorithm. The non-iterative MAP estimation does not need the previous segmentation result. Therefore, the inaccurate segmentation result of former stage does not have effect on the current segmentation stage. Additionally, the non-iterative MAP estimation was designed to adapt the original model so that it does not cause failure from the theory. Experiments show that the improvement is better than the original version and has good results in some benchmark video sequences.