Incorporating appearance and edge features for vehicle detection in the blind-spot area

It is dangerous that changing lane without knowing the information of the other lane in the blind-spot area. We propose a vision based lane changing assistance system to monitor the vehicle in the blind-spot area. So far in the literature, only few results are found using the features of the vehicle to detect the vehicle. Without using features from vehicle, to conclude that vehicles do appear in that area with strong evidence is hard. We use the image features which are directly obtained from vehicle images to detect vehicles possibility in the area. In order to overcome large variation problem due to significant difference in view angle during the process of detecting vehicles in the blind-spot area, we propose a method to combine two kinds of part-based features. After building all the features from training images, we use Adaboost algorithm to choose the best features with better geometric information for detection. The experiments show that our system is reliably in detecting the vehicles in the blind-spot area.