Research On Video-based Vehicle Detection And Tracking Algorithms

At present, motor vehicles are increasing. This causes traffic problems worse and often generates a number of traffic accidents. Man-made illegal events include running red lights, speeding, illegal lane change. Intelligent Transportation System is an intelligent system, through the real-time detection of road traffic flow, it can detect and track vehicles accurately-According to the traffic flow on the road, the system can response quickly. It can reduce road congestion, ease the traffic pressure and reduce the accident rate through taking certain measures. Currently, the intelligent traffic system has been used in vehicle detection and tracking, calculating speed.This paper makes a lot of experiments on motion detection and identification of vehicles. Using Visual C++ 6.0 as the development environment, combined with OpenCV image processing library for development. We can play the video stream and read a frame of the video image by OpenCV. In the process of studying algorithm, the image processing functions is provided by OpenCV library.After capturing video frame by OpenCV library functions, the color space is converted and the video image is processed. The background subtraction and frame difference have advantages and disadvantages. If the combination of these two methods, the performance was complementary, it can not only detect the movement of vehicles and also detect stationary vehicles. At the same time. the result of frame difference gets improved effectively. So this paper extracted the region of interest through background subtraction and the frame difference method. After eroding and dilating the binary image, the image with black background and a full internal structure of vehicles is gotten. Then the contour of binary image is extracted and the bounding rectangle of the contours is calculated. If the bounding rectangle meets certain threshold conditions, it is considered to be vehicle and would be saved to the container which has been initialized. If the bounding rectangle does not satisfy the threshold conditions, it is not vehicle and may be pedestrians, road signs and other interferences and so on. The vehicle detection and identification are completed.In the stage of vehicle tracking, mass center of vehicle reflects the position of the vehicle, and it is the key to extract the mass center correctly. It can not be too large in the adjacent two frames. Color is a feature of vehicles. After establishing the color probability model of vehicle and doing normalized histogram, two cars can be distinguished through comparing the color histogram distance. This article proposed a multi-feature matching method to track vehicles. Rationally extract mass center and color of the vehicle, and then compare the contour with vehicles in containers. If the value is matched, the contour is considered to the same vehicle and then set the same serial number. If the value is not matched, the contour is considered to be a new car and set a new serial number for the car. Multi-feature matching method is proved to be simple and effective.There are different characteristics in illegal behavior of different vehicles. It is the most critical issues to detect and identify the illegal vehicles accurately and timely. In this paper, illegal parking, vehicle reverse driving, illegal behavior of vehicles are studied. The steps are as follows:the system detects whether there are the appearance of moving objects in the region. If so. further confirm whether it is the vehicles moving target of traffic. And then use the appropriate algorithm to determine what kind of illegal behavior in the monitor area that it belongs to and record the appropriate information for subsequent processing.In summary, this paper did follow researches on detection and identification vehicles, tracking and violation detection. In the detection and identification of the vehicle stage, the article proposes to detect vehicles with extracting contours method in the binary image. In the stage of vehicle tracking, the article proposes multi-feature matching method to track the vehicle movements in the video, At last, this paper analyzes and studies illegal parking, vehicle reverse driving, illegal lane changed.