Intelligent Monitoring Of Moving Object Detection And Tracking Algorithms

In the intelligent video surveillance system, target detection and tracking based on video sequence is processed to extract the moving target, and obtain the target in each frame of the video in the location, size and velocity information. It plays an important role. The performance of target detection and tracking will have a direct impact on the validity of intelligent video surveillance system. It is an extremely important part of intelligent video surveillance system.This paper first describes the basic principle of the Meanshift and Camshift algorithm. The search process of Camshift algorithm is given as well as the application of the Meanshift and Camshift in video target tracking. Then Kalman filter is researched according to the condition of target moving so fast. The basic principle and work flow of the Kalman filter is introduced. Also Kalman filter is applied to target tracking. On this foundation this paper puts forward an improved algorithm combined with Camshift. Considering that the color of background is similar to the target or background is complex, this paper adopts the foreground extraction. Here Codebook background modeling method is used to extract the moving object. This binary image is used as a mask to remove the interference of background information and to gain the various prospects information needed. On this basis in order to improve the tracking performance and adapt to more applications, texture information is extracted by using improved local binary pattern algorithm. The tone component and texture information are both used as descriptions of the characteristics of moving target so as to improve the target track robustness and scope of application. Finally the final algorithm is presented in this paper based on the above algorithms, namely an improved target tracking algorithm combined with the foreground extraction and motion estimation. The experiments prove that the algorithm in this paper greatly improves the performance of tracking targets.The main innovations of this paper are combining Camshift with the Kalman filter to solve the problem of the target moving so fast, combining Codebook to distinguish background with foreground and adding texture feature to improve the precision of tracking.