Research Of Video Segmentation And The Application In Industry Park

Recently, due to lack of powerful defending system, plenty of accidents occurred in Industry Park, which caused huge damage to life and property. To improve the capability of handling accidents, governments and Industry Park administration have built emergency response systems by fixing monitor and video surveillance. Existing video surveillance mainly transfer to control center the image data which are observed and judged by the system administrator. However, this kind of video surveillance still has serious problem because it can not analyze status and start emergency strategy automatically. In consequence, how to develop intelligent video surveillance has become a hot research topic. It is also the key technology in modern emergency response system.This paper focuses on video segmentation namely how to extract moving objects reliably. It is the most important part for intelligent video surveillance. The main contributions of this paper include:1) For the video with unspecific objects and minor changes in background, this paper proposed an improved background modeling method, which can compute threshold adaptively in the extraction of any moving objects. Background modeling method is relative simple but has serious misclassification problem. Some methods have been presented to overcome this problem by inocrpoating neighboring relationships of video. However, existing methods have to define empirical values manually, which is not robust for different kinds of videos. This paper uses Ising model to compute energies of neighboring relationships adaptively instead of empirical values. As a result, it makes the improved method is suitable for different kinds of videos. Experiments show the improved method can get expected segmentation.2) For the video with specific objects and obvious changes in background, this paper proposed a novel segmentation method based on recognition for the extraction of specific moving objects. The core idea of the proposed method is to use random fern theory to construct robust classifier according to the features of specified objects. Due to sharing voting rules of ferns, random fern theory can combine with different feature functions to strengthen segmentation result. Experiments show random fern theory can greatly improve segmentation precision greatly over the similar algorithms.3) For the video with unspecific objects and obvious changes in background, this paper proposed a segmentation model by simulating human vision perception. It is suitable for extracting any moving objects. The idea of the proposed algorithm is that human vision can extract any moving objects even with more complex backgrounds. The proposed method first extracts salient signals both in temporal and spatial domain for the video. Then dynamic model combine two kinds of signals to get the region of moving object. Finally, hierarchical conditional random field is used to obtain the final segmentation result. Experiments show it can extract any moving objects in complex backgrounds with the reasonable vision excite signal.4) This paper develops a prototype for the different applying environments in Industry Park. It mainly consists of two levels. The fist application is to detect whether or not moving object occurs in surveillance region. This problem can be resolved directly by video segmentation. The more deep applications are the counting of moving objects and alteration of abnormal objects. Other technologies, such as tracking and recognition, are required for these applications. Finally, some experiment results can prove these applications.