Research & Implementation Of Video Vehicle Detection And Tracking System

Research & Implementation of Video Vehicle Detection and Tracking systemThe video vehicle detection and tracking system is used to get traffic information, such as vehicle flow, vehicle length, vehicle velocity and the roads utilization without destroying the roads. It particularly emphasizes on the management of roads such as the traffic management, road design layout etc. Moreover, there are many problems, such as fast segmentation of moving objects, change of light, vehicle sheltering each other, and vehicle blocking, which make many difficulties to the vehicle video detection and tracking system.This paper provides two vehicle video detection and tracking algorithms based on line-type vehicle video detector and area-type video vehicle detector.The paper includes five parts:The first chapter simply introduces the research background of the system,all kinds of ways to implement the video vehicle detection and tracking system and a few of video vehicle detectors which are used widely today.The first video vehicle detection system introduced by Chapter 2 uses the line-type vehicle detector, background subtraction algorithm. And the detection area which is set by operators to detect vehicles. In our system the background buffer is used to obtain and update the right background in order to use the system at the crossroads. At last, the real results are provided and the performance is analyzed. The system can count moving vehicles and calculate the vehicle velocity correctly.According the shortcomings of the line-type vehicle detector, the video vehicle detection and tracking system introduced by chapter 3 uses area-type video detector which uses the whole area as detection area instead of the area set by operators. The background obtainment algorithm uses high order statistics and low order statistics. Adaptive background update algorithm uses small-step-long value, which is determined by the subtraction of the current and previous picture, to update the background. The best threshold value is gotten through the maximum variance. For the vehicle image edge-broken problem, we use dilation operator. At last, seed filling algorithm finished the vehicle detection. The real results are provided to make a comparison with the first system. The second system can get the whole and accurate background with better performance. When the environment changes, the background updating algorithm is effective. The detected results are almost correct through the series of digital image processes, such as threshold segmentation and seed filling algorithm. But we cannot resolve the vehicle block problem completely.The fourth chapter describes the simple camera adjusting method. Meanwhile, the vehicle tracking algorithm based on Kalman filter is advanced. The moving tracking is achieved by three steps that are the matching step, emendation step, prediction step. In order to reduce the compute complexity, the X and Y coordinates are assumed that there is no relationship between them. So the X and Y directions can be tracked separately using the Kalman filters. The results are proved the efficiency of the method.At last, the design and implementation of the video vehicle detection system based on line-type video detector are introduced.The paper provides many ways to resolve the difficult problems of video vehicle detection and tracking system. The system can be used in real-time environment. Moreover, the background obtainment and renewal algorithms are viable and results are perfect.