A HOG Feature and SVM Based Method for Forward Vehicle Detection with Single Camera
Xing Li, Xiaosong Guo · 2013
Vehicle detection is very important for automotive safety driver assistance system. This paper focused on improving the performance of vehicle detection system with single camera and proposed a HOG feature and SVM Based method for forward vehicle detection. The shadow underneath vehicle is the most important feature, so it can be utilized to detect vehicle at daytime. The shadow was segmented accurately by using histogram analysis method. The initial candidates were generated by combining horizontal and vertical edge feature of shadow, and these initial candidates were further verified by using a vehicle classifier Based on the histogram of gradient and support vector machine. The experimental results show that the proposed method could be adapt to different illumination circumstances robustly and has a detection rate of 96.87 percent and a false rate of 2.77 percent under normal light condition.