Research On Moving Objects Detection And Tracking Methods Based On Video

With the development of digital image processing, artificial intelligence, pattern recognition and other disciplines, video-based moving target detection and tracking technology is increasingly used in intelligent transportation, security surveillance, machine vision and other fields. The prospect of the research is good, and recent research continues to heat up, the depth learning instead of the traditional method of manual features plus classification has made good progress in the areas of detection and identification of the image. In this paper, the application background is how to get urban traffic information from intelligent transportation system, and the research object is the output video images of monocular camera with fixed scene, we study the existing moving target detection and tracking algorithms and give some improved methods in order to improve the accuracy and real-time performance of the algorithm. The paper is divided into three parts, and the main contents are as follows:1. Background modeling and moving target detection. Background modeling is the key point of moving target detection, in order to adapt to the effects of ambient light changes and shadows of moving target in outdoor traffic scenes, an improved codebook-based methods of moving target detection is presented. With the insensitivity of the changes of periodic environment illumination, we improved the detection result under the case that the light changes slightly. To solve the shadow problem, we construct the shadow codebook model to suppress the interference of moving target shadow.2. Image preprocessing. Before detecting, the video sequences should be smooth-filter processed in order to eliminate noise interference. And after detecting, the binary image of moving objects exist a lot of isolated noises and small connected regions, then we using morphological expansion and corrosion methods to eliminate isolated noises and fill the hollow inside the moving objects. Also, by setting the threshold of the connected areas, we can remove uninterested objects such as pedestrian, bicycle and so on. 3. Prediction and tracking of moving targets. First, we extract the target centroid and external rectangle, and initialize the kalman prediction model to predict the region in the next frame by using the method based on kalman prediction and feature matching. Then, calculating the feature distance between the target and the objects in the predicted region of the next frame, and setting match threshold in order to realize target tracking. To deal with the occlusion in the tracking process, we handle it as background occlusion or occlusion between two targets.To handle the issues of shadows, occlusion and other problems in the process of moving target detection and tracking, an improved method considering the timeliness and accuracy has been proposed in this article, and the method has been tested with traffic video.