Efficient vehicle detection with adaptive scan based on perspective geometry

Vehicle detection is an important research problem for Advanced Driver Assistance Systems to improve driving safety. Most existing methods are based on the sliding window search framework to locate vehicles in an image. However, such methods usually produce large numbers of false positives and are computationally intensive. In this paper, we propose an efficient vehicle detection algorithm that dramatically reduces the search space based on the perspective geometry of the road. In the training phase, we search a few images to locate all possible vehicle regions by using the standard HOG-based vehicle detector. Pairs of vehicle candidates that satisfy the projective geometry constraints are used to estimate the linear vehicle width model with respect to y coordinates in the image. Then an adaptive scan strategy based on the estimated vehicle width model is proposed to efficiently detect vehicles in an image. Experimental results show that the proposed algorithm provides improved performance in terms of both speed and accuracy compared to standard sliding-windows search strategy.