Research On Target Detection And Tracking In Video Surveillance

Target detection and tracking technology is one of the core issues in the field of computer vision, video surveillance and pattern recognition. The main goal for achieving tracking target position information and movement parameters(such as speed, acceleration, etc.) and draw the target trajectory in image sequence, so as to get prepared for image understanding, target behavior analysis which are more advanced tasks in video surveillance. This paper takes the vehicle on the road as research object, and focus on the research on vehicle tracking in different scenarios. The main research is based on the idea of tracking by detection that the detector enhances the robustness of tracker and the result of tracker is used as the samples to train detector online. The thesis mainly consists of three parts: the study on recursive tracking algorithm, the detector and the online learning method.First, we design the tracker module. This paper compared three different algorithms, and after analysis and improvement, a fusion of optical flow and Mean-shift method is proposed to track targets. The classic optical flow method is compared in experiments and our method is proved to get a better result.Secondly, a target detection module is designed. We, respectively, try the Haar-like feature classifier and cascade classifier to detect target. In the cascade classifier, we improved sliding window search mechanism, and applied Vibe foreground extraction algorithm as the second stage in cascade classifiers. The third stages of cascade classifier is implemented by the random fern method. In the last stage of the cascade classifier, we use template matching method to achieve the detector location.Then, the online learning module is designed. Based on the principle of motion continuity, we regard the sliding windows adjacent to tracking result as positive samples, and the rest as negative ones, and then the detector parameters are learned online.Finally, our experiments use PETS2000 database and the actual traffic monitoring video to verify the proposed algorithm. The results show that in a number of different scenarios, the method achieved a certain accuracy, but the tracking speed cannot meet the real-time requirements. Meanwhile, the proposed algorithm performs well in the situation that rotation, occlusion, deformation and scale changing occurs.