基于分级树状级联分类的双特征夜间行人检测

doi:10.3969/j.issn.1002-0268.2014.07.023

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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.

Key words： ITS pedestrian detection Haar-like feature Edgelet feature binary tree architecture

Received: 25 January 2015

Fund:Supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region of China(No.2013211B36)

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	ZHANG Rong-hui
	ZHOU Jia-li
	YOU Feng
	ZHOU Xi
	PEI Yu-long

Cite this article:

ZHANG Rong-hui,ZHOU Jia-li,YOU Feng, et al. A Pedestrian Detection Method Based on Hirarchical Tree Cascade Classsification at Nighttime[J]. Journal of Highway and Transportation Research and Development, 2015, 9(2): 77-83.

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http://manu27.magtech.com.cn/Jwk_gljtkj_en/EN/10.3969/j.issn.1002-0268.2014.07.023 OR http://manu27.magtech.com.cn/Jwk_gljtkj_en/EN/Y2015/V9/I2/77

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