Improving vehicle detection by adapting parameters of HOG and kernel functions of SVM

Currently, vehicle detection suffers from low performance in terms of accuracy and time costs in real-time application. Histograms Oriented of Gradients(HOG) and Support Vector Machine(SVM) are popular methods used to address these problems, however, while they can give high accuracy, detection is still too slow for real-time application. The V-HOG method has previously been proposed to reduce detection time in real-time application by adjusting HOG structures. Although V-HOG detection is faster than that of HOG, the accuracy is lower. Therefore, this paper proposed to improve accuracy and classification time by adjusting HOG parameters and SVM kernel functions. The experimental results showed that the proposed method results in 100% accuracy and supports real-time application.