Research Of Video Object Extraction And Tracking Approach

Classical video coding standards such as H.26x and MPEG-1/2 are frame-based techniques, and no segmentation of video scenarios is required. Their high compression performance makes them widely used in video applications. With the proliferation of multimedia information, people are no more satisfied with simple navigation of video contents, but require object-based functionalities. Therefore, MPEG-4 introduces the concept of video object to support content-based functionalities. MPEG-7 defines a universal and normalized description of various multimedia objects. According to the MPEG-4 verification model, video sequence must be segmented into semantic video objects. Their motion, shape and texture information are coded respectively. The main values are: improved coding efficiency by allocating different bit rate to different video object in accordance with their importance to human visual system; object-based scalability so as to obtain better visual effect at low bit rate applications; content-based storage, interactivity and retrieval organizing video content according to video object.Though MPEG-4 introduces the concept of video object, it does not specify any concrete techniques for obtaining video objects from video sequence. On one hand, the semantic homogeneity of video object is hard to be modeled by any low level features, which makes a general segmentation algorithm for various video sequences is still a classical problem to be resolved; On the other hand, priori knowledge can often be utilized for specific application.Author presents two strategies of obtaining VOP. In order to eliminate the effect caused by noise, complex motion and uncovered background in the process of segmenting video objects, a new method based on the intersection between three edge of frame change is proposed, We use two frames at a distance of k for frame change instead of two successive frames, thus the articulation motions and slow motions can be processed properly; The uncovered background due to the motions of video objects can be effectively eliminated by the intersection between three edge of frame change, and the edge of frame change caused by the background noise can be eliminated at the same time. The outline can be obtained by scanning the intersection in row and column, we need to repair the part outline point before filling in outline to form the binary image. Finally, morphology processing was performed on the binary image obtained from the previous step to get the segmentation result. Experiment results show that the new algorithm can segment video objects accurately and automatically. For the algorithm of based on hausdorff distanced, author propose a new distance measure and track occluded & revealed object. The algorithm has more robustness than other modified Hausdorff distance.With the research of segmentation and tracking of video object, we take note of algorithms' applicability and relation between segmentation and tracking. In the strategies, author adds preprocessing and post processing to ensure the validity of the algorithms.