The Research On Moving Object Detection In Intelligent Transportation System

Automatic video surveillance is an important research area in computer vision. It has promising prospect in the applications of collecting traffic information, and monitoring the important areas, such as bank, hotel, and railway station.Upon the background of detecting and tracking moving vehicles in an intelligent video-based road traffic surveillance system, in this thesis, we focus our research on some hard issues in detecting moving objects, such as updating background image, eliminate the disturbances of moving cast shadow and vehicle headlight, and propose a robust algorithm of detecting and tracking moving vehicles on road. We also apply our algorithm to an applicable road traffic monitoring system. The main contents can be listed as follows:1. After analyzing the properties of the natural illuminating system, a region-based background and natural illumination model is set up using the histogram of image difference and RLS filter. On the basis of that model, we propose an adaptive algorithm of judging and updating background image. The experiment indicates that the algorithm can suppress noise effectively and adapt to the variation of the illuminating condition robustly.2. Aiming at eliminating the disturbance of moving cast shadow in detection, a robust algorithm of detecting moving vehicles is proposed. By choosing the histogram of image difference as features, a Support Vector Machine (SVM)-based classifier, which has high generalization performance, is used to segment shadows and vehicles. The result of experiment is rather satisfying. We also apply the classifier to eliminate the disturbance of vehicle headlight at night and it also works. In the moving vehicle detection algorithm, the shape of a vehicle is well represented by a parallelogram, which makes the information of the vehicle extracted from the image reliable.The detection algorithm is applied in the vehicle tracking. By assuming that the vehicle moves with constant acceleration from current frame to the next, a Kalman filter model is used to tracking and predicting the trace of a vehicle. The detection algorithm has some advantages such as an explicit and applicable3. model, the low computation cost during detection and its robust adaptation to the variation of environment. The interaction between the phases of detection and tracking improves the reliability of the whole algorithm.4. An applicable road traffic monitoring system is developed on the basis of our detection algorithm. The design of the system architecture, functional modulates and user interfaces is introduced in the thesis. The system is tested for its real-time performance, reliability and stability and the experiment shows that such traffic monitoring system can work real-timely under any weather and illuminating condition, providing comprehensive and accurate statistics data of traffic flow. It satisfies the demand of real environment application.The proposed algorithm of detecting and tracking moving vehicles in this thesis, which has important theoretic meaning and application value, is a universal method in the research area of video surveillance. It can be easily generalized to the applications with different situations, and will have a wide application prospect as well.