Foreground Extraction and Motion Recognition Technology for Intelligent Video Surveillance

With the rapid development of computer technology and network technology, it has become possible to build a large-scale networked video surveillance system. The video surveillance system has become a new type of infrastructure necessary for modern cities. In this paper, the problem of foreground extraction and motion recognition in intelligent video surveillance is studied. The three key sub-problems, namely the extraction of motion foreground in video, the deblurring of motion foreground and the recognition of human motion, are studied and corresponding solutions are proposed. A background modeling technique based on video block is proposed. The background is modeled at the block level, which greatly reduces the spatial complexity of the algorithm. It solves the problem that the traditional Gaussian model (GMM) moving target enters the static state and is integrated into the background process. The target starts to move for a long time and there are ghosts and other problems, which reduce the processing efficiency of the lifting algorithm. The test results on the Weizmann dataset show that the proposed algorithm can achieve high human motion recognition accuracy and recognition with low computational complexity. The rate can reach 100%; the local constrained group sparse representation classification (LGSRC) model is used to classify it. The experimental results on Weizmann, KTH, UCF sports and other test datasets confirm the validity of the algorithm in this chapter. KNN, SRC voting classification accuracy.