A Vision-Based Approach to Collision Prediction at Traffic Intersections

Abstract

Monitoring traffic intersections in real time and predicting possible collisions is an important first step towards building an early collision-warning system. We present a vision-based system addressing this problem and describe the practical adaptations necessary to achieve real-time performance. Innovative low-overhead collision-prediction algorithms (such as the one using the time-as-axis paradigm) are presented. The proposed system was able to perform successfully in real time on videos of quarter-video graphics array (VGA) (320 /spl times/ 240) resolution under various weather conditions. The errors in target position and dimension estimates in a test video sequence are quantified and several experimental results are presented.