The Research Of The Improved Tracking Algorithm Based On TLD

With the rapid development of computer technology and artificial intelligence, computer vision have a wide range of use in monitoring security, identity authentication, medical diagnosis, industrial inspection and many other fields. Image as an important source of human knowledge which contains a wealth of information, so the ultimate goal of development of computer vision is to realize the computer understand the objective environment in image and to make decisions or other processing forward to replace artificial.Target tracking as a research direction of computer vision which caused wide attention. In this paper, first of all, we divided the procedure of the tracking algorithm into four steps: feature extraction, motion model, observation model and model update, and introduced the methods which commonly used in these four steps. Then we focuses on Tracking- Learning- Detection(TLD) Tracking algorithm, TLD is a single target for longtime Tracking algorithm, and it solves the problem of how to acquisition the target when it’s disappear from the track field of vision and then reappear again, this algorithm has attracted great attention since 2011 when it’s put forwards, many researchers from home and overseas proposes the improvement strategy.Based on the deep understanding of the TLD tracking algorithm, this paper introduces the tracking module, detection module and learning module of the algorithm. Through the analysis of the limitations of Kalman filtering algorithm, this paper introduced the Multi-Innovation theory and we added the Multi-Innovation Kalman filtering algorithm(MIKF) into the TLD tracking algorithm, and proposed an improved TLD algorithm based on Multi-innovation Kalman Filter(TLD_MIKF). TLD as a tracking algorithm which can only works in single camera to tracking a single target, and the tracking view has great limitations. This paper puts forward the multiple camera tracking algorithm based on TLD. We improve the robustness of the algorithm and implement the continuous tracking target under the condition of multiple cameras. The specific research in this paper is as follows:(1) To solve the problems of tracking failure caused by blocked target and low tracking precision in the TLD algorithm, an improved TLD algorithm based on Multi-innovation Kalman Filter is proposed in this paper. The improved algorithm models the target before tracking, uses the results of TLD tracking algorithm as the current observations and then combines with the predicted values of the Multi-innovation Kalman Filter to optimize the tracking results. The experiments show that the improved algorithm of TLD has higher precision than the original TLD algorithm, and it’s able to predict the position of the target when the target is occluded.(2)In view of the TLD tracking algorithm can only process in the frame which input from a single video stream. A multiple camera tracking algorithm is proposed in this paper which is based on TLD. It’s combined with Kalman filtering and the form of a bulletin board. The algorithm abstract the layout of distributed cameras as an undirected graph and determine its topology artificial. The algorithm publish the information about tracking process real-time to the bulletin board which is maintain in the center server. Kalman filter algorithm is used to deal with block and predict the direction of the moving target. Tracking a single target in the multiple camera environment is realized in this algorithm, and it can also record the target trajectory. The feasibility of the algorithm is verified by experiment.(3)After proved the effectiveness of the improved algorithms through experiment which put forward in this paper. We designed and implemented the tracking system based on TLD which tracking a single target in the area of multi camera, the system can get the video stream by camera or video file in two channels, and it realize the target tracking under the environment of either single camera or double camera. The system can select the target search area, such as tracking the vehicle in selection area artificially.