A Novel On-Road Vehicle Detection Method Using <inline-formula> <tex-math notation="LaTeX">$\pi$</tex-math></inline-formula>HOG

Abstract

In this paper, a new on-road vehicle detection method is presented. First, a new feature named the Position and Intensity-included Histogram of Oriented Gradients (PIHOG or $\pi$ HOG) is proposed. Unlike the conventional HOG, $\pi$ HOG compensates the information loss involved in the construction of a histogram with position information, and it improves the discriminative power using intensity information. Second, a new search space reduction (SSR) method is proposed to speed up the detection and reduce the computational load. The SSR additionally decreases the false positive rate. A variety of classifiers, including support vector machine, extreme learning machine, and $k$ -nearest neighbor, are used to train and classify vehicles using $\pi$ HOG. The validity of the proposed method is demonstrated by its application to Caltech, IR, Pittsburgh, and Kitti datasets. The experimental results demonstrate that the proposed vehicle detection method not only improves detection performance but also reduces computation time.