Research Of Fast Illegal Vehicles Detection Methods In Intelligent Transportation System

Intelligent Traffic System is the research topic of the world transport sector, involving multiple areas such as information technology, data communications, transmission technology and electronic control technology, sensor technology and computer processing technology. It plays a role in a wide range of real-time, accurate and efficient management system, aiming at the close coordination of people, vehicles and roads to improve the efficiency of transportation, environmental quality and energy efficiency.As traditional intelligent traffic detection systems have poor real-time capability and can be easily influenced by illumination variations, which leads to a problem in identifying vehicles peccancy and capturing the image about them. A robust and fast vehicles peccancy detection and tracking method is proposed based on color difference histogram algorithm and Kalman filter. Background fuzzy matching method is used to select initial background images at first. Then color difference histogram algorithm which is robust to environmental change is used to detect moving targets. And then, Kalman filter is used to track and predict the mass centroid of the moving targets in order to detect the same target in the predictive zone. At last the trajectory of the mass center is used to identify them and capture them.After the experiment through the detection of the actual roadway scene under different environments and the video, the results demonstrate that the proposed method can identify vehicles peccancy fast and accurately. Two applications which are monitoring of illegal occupation of bus lane and retrograde based on the algorithm have been developed and have become part of the intelligent traffic system.