Extended Kalman filter based pedestrian localization for collision avoidance

The practical driving safety assistant system should be able to estimate the possibility of pedestrian-vehicle collision, which includes pedestrian detection and localization as well as collision prediction. Until now, many works concentrated on pedestrian detection and achieved some progress. For collision prediction, it is essential to locate the pedestrian precisely; however, the localization problem still needs to be further studied. At present, most researches adopted expensive equipments (e.g. millimeter wave radar and laser scanner) to run away from the difficulties; and many others used multi-cameras to solve this problem. In our previous work, we proposed a low-cost pedestrian detection system with a single optical camera, which performanced well in pedestrian detection. Basing on the detection system, an extended Kalman filter based pedestrian localization model/methodology is proposed in this paper. The localization model sets up proper relation between state vector and observation vector and chooses proper initial state for the Kalman filter using perspective projection principle, which guarantees the proposed filter to estimate the location of pedestrian quickly and actually. The experimental results have validated that the accuracy of the proposed localization model/methodology may meet the requirements of a practical collision avoidance system.