Vision-based vehicle detection and inter-vehicle distance estimation for driver alarm system

In this paper, we propose a robust real-time vehicle detection and inter-vehicle distance estimation algorithm for vision-based driving assistance system. The proposed vehicle detection method uses the combination of multiple vehicle features, which are the usual Harr-like intensity features of car-rear shadows and additional Haar-like edge features. The combination of two distinctive Haar-like intensity and edge features greatly reduces the false-positive vehicle detection errors in real-time. And, after analyzing two inter-vehicle distance estimation methods: the vehicle position-based and the vehicle width-based, we present a novel improved inter-vehicle distance estimation algorithm that uses the advantage of both methods. Various experimental results show the effectiveness of the proposed method.