Research On Detection And Tracking Algorithm Of Moving Targets In Static Scenes

Target detection and tracking is a key part in the field of computer vision, it has important application value in many aspects such as military,vision,video, traffic,medical treatment and so on.Recently, the main problem of target detection and tracking is how to find the target tracking accurately.Based on the existing research results, this paper improves the algorithm of target detection and tracking, In this paper, on the basis of the existing research results, the target detection and tracking algorithm is improved, and makes an experimental simulation of the improved algorithm before and after. The main improvements are as follows:(1) Target detection algorithm of frame difference method combined with canny operator. Frame difference method has the advantages of simple algorithm, not affected by the external factors, canny operator can get the contour information of the image better than other operators. In this paper, the canny operator is used to detect the contour of the current image, and the frame difference method is used to obtain the target area,the common area is the position of the target in the current frame. The experimental results show that the target area obtained by the improved detection algorithm is more complete.(2) Meanshift algorithm of adaptive tracking window. There are many defects in the traditional meanshift algorithm: In the process of tracking, if the size and direction of the target changes, it can't be good tracking. Based on the above problems, the meanshift algorithm of adaptive tracking window is proposed. This method uses the frame difference method with the canny operator to detect the moving object, and principal component analysis is used to calculate the direction of the target, update the tracking window in real time. The experimental results show that compared with the traditional meanshift algorithm, the tracking stability of the improved meanshift algorithm has been improved.(3) Target tracking algorithm based on meanshift algorithm of adaptive tracking window fusion kalman filter. In the process of tracking, when the target encounter occlusion,the tracking of meanshift algorithm of adaptive tracking window becomes unstable. In order to solve this problem, kalman filtering is added to the step of target prediction,meanshift algorithm of adaptive window and kalman filtering fusion was achieved. The experimental results show that the improved algorithm can improve the anti occlusion performance and the stability of the tracking is improved.