A Pedestrian Detection Method Based on Hierarchical Tree Cascade Classification at Nighttime

Abstract

Illumination, for pedestrian detection at nighttime is weak, and detection is easily affected through variations in illumination. Thus, a bicharacteristic method of pedestrian detection at nighttime based on hierarchical tree cascade classification is presented according to “coarse-to-fine” principle. The proposed method consists of two stages of cascade classifiers. Coarse cascade classifiers are constructed in complete binary tree architecture. These classifiers use Haar-like features for the rapid identification of candidate pedestrian areas. By contrast, fine cascade classifiers have a parallel structure. Edgelet features are used for detection along three parts: the head-shoulder, trunk, and leg parts of candidate pedestrian areas. Bayesian decision-making is adopted to achieve pedestrian target detection and a comprehensive analysis of the detection results from these three parts. Experiments show that the proposed method has high accuracy, ideal real-time performance, and strong reliability. Research works, such as the present study, can serve as reference for vehicle safety driving technology.