Integrated real-time vision-based preceding vehicle detection in urban roads
This paper presents a solution algorithm for the real-time operation of vision-based preceding vehicle detection systems. The algorithm contains two main components: vehicle detection, and vehicle tracking. Vehicle detection is achieved by using vehicle shadow features to define a region of interest (ROI). The methods such as histogram equalization, ROI entropy and mean of edge image, are adopted to determine the exact vehicle rear box. In such way, vehicles can be detected in video images. In the vehicle tracking process, the predicted box is verified and updated; and certain important parameters such as relative distance or velocity, the number and type of the tracked vehicle are extracted. The proposed solution algorithm has been tested under different traffic conditions in Hong Kong urban areas. Test results demonstrate that the proposed solution algorithm has a good detection accuracy and satisfactory computational performance.
vehicle detection vehicle tracking feature extraction shadow boundary