1. Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Science, Urumq Xinjiang 830011, China;
2. School of Civil Engineering and Transportation, South China University of Technology, Guangzhou Guangdong 510640, China;
3. School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin Heilongjiang 150090, China
A Pedestrian Detection Method Based on Hirarchical Tree Cascade Classsification at Nighttime
ZHANG Rong-hui1, ZHOU Jia-li2, YOU Feng2, ZHOU Xi1, PEI Yu-long3
1. Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Science, Urumq Xinjiang 830011, China;
2. School of Civil Engineering and Transportation, South China University of Technology, Guangzhou Guangdong 510640, China;
3. School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin Heilongjiang 150090, China
摘要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. Edge let 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.
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. Edge let 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.
张荣辉, 周佳利, 游峰, 周喜, 裴玉龙. 基于分级树状级联分类的双特征夜间行人检测[J]. Journal of Highway and Transportation Research and Development, 2015, 9(2): 77-83.
ZHANG Rong-hui, ZHOU Jia-li, YOU Feng, ZHOU Xi, PEI Yu-long. A Pedestrian Detection Method Based on Hirarchical Tree Cascade Classsification at Nighttime. Journal of Highway and Transportation Research and Development, 2015, 9(2): 77-83.
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2015, Vol. 9 
