Moving Object Detection Based On Belief Propagation Algorithm

With the development of hardware and software of computer, computer vision technology is attracted more and more attention by people. Especially in these fields, which include military affairs,aviation spaceflight,CAD,smart android and so on, computer vision technology is used widely. The vision technology which is based on video frequency image sequences is the most important computer vision technology, so it has became hot points and difficult points in investigate works of people. Motion objects detection and tracking which is based on video frequency ,is also the basic and key of computer vision technology.First, on the basis of studying related literatures, methods and development of motion object detection are introduced. Second, the related concept of the basic principles about Markov Random Field (MRF) and Expectation Maximum(EM)are introduced and methods are studied in detail.In this paper, a motion object detection method based on belief propagation is proposed, which is based on the time-differenced image of the moving object detection method. In the method, pixels in the image will be divided into two types of background and target, according to the difference image. But there are some noises in the result and a smooth algorithm to remove the noise is needed to make the result more precisely. In the time-differenced image, motion pixels are mainly focused on the edge of the larger gradient, and the goal is often more empty, so a belief propagation algorithm with a larger effort to disseminate the trust is chosen to smooth, which is combined with a gradient of space-gradient operator. Gradient operator is used to constrain smooth and more accurate results can be got. Results from Expectation Maximum(EM) algorithm for classification are smoothed in order to achieve the target partition movement.Finally, the algorithm describes the realization of simulation, experiment is performed and results are given. It proves that the algorithm has a better performance for motion objects detection.