Research And Application Of Motion Segmentation Based On Parameter Estimation

With the development of computer technology, the object of study has been transferred from single static image to motorial image sequences. Motion segmentation which is an initial and necessary stage in many video analysis applications is the process of obtaining moving objects by dividing video frames into regions that have different motions. However, it is still a challenge to provide an accurate and efficient motion segmentation method.This paper first introduces the production and development of motorial object segmentation technology and its present situation. Then discuss the basic research method of this field and give the systematic classification to these methods. The following part of the paper, a novel video motion object automatic segmentation algorithm based on Markov random field has been studied in detail. This algorithm use Gaussian mixture distributions to describe the different images of video sequence and make some improvement to the standard MAP algorithm by using the fast method to compute the posterior marginal. First of all, initial segmentation of video is used to obtain the number of initial motions and the corresponding initial parameters of the motion model. And then it connects every motion area and motion model to estimate multi-motion area synchronously by updating the model parameters, consequently achieve the aim of segmentation. The experiments show that the method is effective.In addition, based on above work, the spatial segmentation is provided as an observed field of the image and the field is initialized as the temporal segmentation result. Then they are connected by the model restraint condition to obtain the final labels. With the experiment results, the above algorithm has good performance to motion object segmentation.