Video Object Segmentation Technology And Application

In the information society, by providing the most extensive and comprehensive information elements, video content is more and more popular in the industry of modern traffic, network media and computer vision. However, the original video often contain too large amount of information. Some of them even for most of the information are not meaningful to industry specific applications. So, How to extract useful information, to reduce the amount of information from the video will be an important problem seriously related with practical applications. For example, in traffic control and safety monitoring video, only when the main information of the video (Mainly referring to the information of moving object) is extracted the work on monitoring road safety and the production environment will be well done.At present, video object segmentation technology developed in recent years is an important foundational technology in the area of extracting effective video information. The technology has been widely used in traffic streaming video monitoring, industrial automation control, security, network and multimedia interactive video compression coding. In this paper, video segmentation technology and its application were researched. And an improved background extinction method based on Gaussian mixture model and wavelet theory was proposed. The method effectively reduced the noise and holes in the segmentation and improves the accuracy of moving object.In this paper, the major work done is as follows:1. A variety of segmentation algorithm existed were researched and realized. The common tools and methods used in the video segmentation from static image processing technology, such as filtering method, morphological operators and static image segmentation method; video object segmentation technology, especially the common methods in the automatic segmentation methods.2. An improved background extinction was researched and proposed. The background extinction segmentation method based on Gaussian mixture model had the disadvantages of holes and inaccuracy in the segmentation edge. An improved background extinction method which contained pre-segmentation and post-processing was proposed. Pre-segmentation was based on Gaussian mixture model first, and then color model and contrast model was used in this method. Post-processing used several methods, such as median filtering, pyramid decomposition of wavelet theory, morphological open computing, to eliminate random noise and holes. This method effectively solved the disadvantages of the original background extinction method. When it eliminated holes and noises the edge was well maintained.3. Practical application of video segmentation was researched. As an example of road monitoring in the modern traffic, the method proposed in this paper was used first, the cars was segmented, and then contour approximation was used to labeled the object, so real-time tracking and auxiliary monitoring was realized. In the end, the method proposed in this paper, original background extinction method and frame differencing method were compared to prove the effectiveness of the former.