Robust vehicle extraction in video-based intelligent transportation systems

In recent years, video-based Intelligent Transportation Systems (ITS) have been of major importance for enforcing traffic management policies. We propose a real-time and effective method for detecting vehicles from a sequence of traffic images taken by a single roadside mounted camera. The proposed algorithm includes three stages: first, extract moving object region from the current input image by background subtraction method, second, eliminate moving cast shadow which is often caused by moving vehicle and, at last, detect vehicle so that there can be a unique object associated with each vehicle. The proposed method has been tested on a number of monocular traffic-image sequences and the experimental results on the real-world videos show that the algorithm is effective and real-time. The correct rate of vehicle detection is higher than 90 percent, independent of environmental conditions.