With the development of intelligent vehicle technology, vehicle detection based on vision analysis has become an research hotspot in forward collision warning system development. Aiming to solve the existing problems in the current vehicle detection methods, for example, the detection accuracy is sensitive to the variation of illumination and object angle, we propose a forward moving vehicle detection method according to multiple vision clues fusion. Firstly, we locate the rough position using vehicle bottom shadow detection. The shadow is detected using an adaptive threshold image segmentation approach twice. Secondly, the symmetry of vehicle body and the perspective of camera field of view are both referenced to remove the inaccurate location in the first stage. The proposed method has been tested on several videos recorded in real urban conditions. Experimental results show that our method achieves 93.67% average detection accuracy in daytime, and its processing speed is more than 25fps. The proposed method has certain application prospects for improving the vision based forward collision warning system performance.
Vision-Based Vehicle Detection for a Forward Collision Warning System