Moving Object Tracking Method Based on SVM and Meanshift Tracking Algorithm

In this paper, a video moving object tracking method based on SVM and Meanshift tracking algorithm is proposed. The location of the tracking object is selected in the initial image of the sports video, the feature vectors of the object and background around the tracking object is obtained, the object and background feature vectors are used to train the SVM binary classifier, and the classifier is used to classify the next video image to track the object location and the background image to obtain the confidence map. Use the Meanshift tracking algorithm to get the current tracking object center position within the confidence map range, move the center position of the object frame and background frame to reach the object position, zoom the object frame at a 10% scale, and select the best one to adapt to the change of object size. Determines if the last frame of the video has been tracked, and if not, train a new SVM classifier using the object and background pixels at this time to track the next frame of the video until the entire video sequence image moving object tracking task is completed. The experimental results show that the proposed method can track the moving objects in the video real-time and accurately.