Research On Algorithms Of Video Surveillance Image Processing And Detection

The development of Intelligent Security Systems(ISS) is an efficient way to solve the problems of people's life and property security threat, difficulties of data processing in later major security incidents investigation. The development of ISS is also the trend of future security systems. With the simple configuration, maintenance, and more accurate data acquisition, video-based ISS has received increasing attention by researchers. The quality detection of the video, video concentration and video abstract technology is the basis and premise of the intelligent security system, target recognition is the ultimate goal of intelligent security system. This subject was mainly focus on the following topics, the quality detection of the video, video concentration, video abstract and target recognition.Firstly, we focus on several representative image quality problems in this paper, including lack of video image, video image occlusion, video image Gauss noise, video image color cast, video image streak interference problems and so on. And in this paper, a new method to detect streak noise with FFT is presented in order to handle the variety and not ease to be detected in a unified way of streak noise. The experiment results showed that the improved method can detect random generated fringe images effectively with higher accuracy.Secondly, this paper mainly researched the video concentration and abstract method commonly used, combined with the characteristics of the security video image, aiming at the defects of traditional video fast forward algorithm cannot save the effective video information, proposes a new static key frame reservation video concentrate technology; aiming at average method and traditional covering method showed not clear moving target, the overlapped part is occluded cause source image information loss, this paper proposes a video group improved cross fusion abstract technology. Experiment results showed that the new static key frame reservation video concentrate technology compared to the artificial watching video has more than 10 times speedup, at the same time it can keep the useful information of the moving target in video, discarding the useless stationary frame. Video group improved cross fusion technology can ensure the fusion video's foreground moving target are clearly visible, and moving target coincidence part adopts blur measures, ensure to see all the moving target information in the overlap region.Finally, in the image and target recognition. This paper focuses on the algorithm of CRBM network learning algorithm, and puts forward three kinds of different network connection strategies, through training standard image library, comparing effect of three different network connection method, and at the same time network parameter set strategy is also discussed. Experiment results showed that using the full connection method has the highest recognition accuracy, for the COIL-100 database, network design for inverted Pyramid shape will have better recognition.