Vehicle Detection for Forward Collision Warning System Based on A Cascade Classifier Using Adaboost Algorithm

This paper proposed a monocular vehicle detection for forward collision warning system. We use the active-learning framework to train a cascade classifier and use a two steps vehicle detection. We used five test data to quantify our detection performance, analyzing the two-stage vehicle detection improvement, and the overall detection rate and the false detection rate. In a good light condition, the detection rate and the false detection rate can achieve 0.967 and 0.122, respectively. Our system can achieve up to 45 frames per second on Intel core i7-6700 CPU.