A Method of Surveillance Video Structured Based on Gaussian Mixture Model and Support Vector Machine

Abstract

Since that the surveillance video is an unstructured media, it is not beneficial for the video intelligent retrieval and mining. An approach that is based on Gaussian mixture model and support vector machine has been put forward in this paper, which can make the video of surveillance scene structured. First, it constructs Gaussian background modeling to video scene, and isolates the motion object layer. Second, the visual perceptive information from moving object can be extracted by the angular point detecting method. Third, the multi-granularity perceptive feature of the object can be extracted by the object centroid-centred. Last, a 2-level SVM classifier should be build. By this classifier the semantics can be labeled to the moving objects, and then the structured description of the scenes can be obtained. The experimental results show that the presented method can avoid the interference caused by luminance changes and the motion of the leaves effectively. It is suitable for the video of surveillance scene in structured analysis application and can be a technical support for the intelligent retrieval and mining of video contents.