Research On Population Statistic Algorithm In Video Surveillance

At present society is a rapidly developing society. Along with economical development, progress of the hardware is also constantly. With the modernization of people's lives, the application of video surveillance systems has become ubiquitous. The future of video surveillance system development focus on enhance the degree of automation and minimizing manual operations, so make full use of computer image processing technology is one of the most important way. In particular, in various public places, the crowd movements become more frequent. How the crowd thoroughly enjoyed themselves in public places, effective management and control, is currently a key issue to be resolved. As a result, the counting of the people in dynamic scenes under the statistical methods came into being.This article describes the development status of target detection and tracking technology. Study the basic theory of tracking algorithm and main detection, analysis of the advantages and disadvantages of current algorithm. On this basis, combine character segmentation and crowned tracking to build people counting system. Our system has been greatly enhanced at calculation of speed and stability. System mainly includes four components:target detection, a rough segmentation, single count, the crowd count. Firstly, the regional campaign on the monitoring target detection extract and filter out noise, the background regions have been identified as the crowd of region segmentation and annotation, and then separately track the population and single count, the final data to be added to the exact number.Completion of background updating using the frame difference, foreground extracted using background difference. In the segmentation process, in order to avoid the impact of occlusion, we consider similarity between tracker and measure not only by comparing central positions but also height and width of the bounding box:using overlapping area. The overlapping rate matrix functions as similarity and can reduce computing complexity effectively. Then we used an area of statistics on the number of people to get a rough statistics, and using merging and splitting to compensate weakness of multiple-human segmentation from handle occlusion.From experiments we can see, this paper builds a real-time, effective monitoring system that can monitor the right to enter characters within the scope of quantitative statistics. Using the Coarse-to-fine segmentation, instead of overlapping frame rate to compare the distance between the centers of similarity, reducing the computational complexity; in the population statistics of the time, according to the relevance of compensation due to character segmentation result of misjudgment. Experiments show that the system in improving the real-time, while also taking into accounts its accuracy.