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.


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    Title :

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


    Contributors:
    Zhang, Rong-hui (author) / Zhou, Jia-li (author) / You, Feng (author) / Zhou, Xi (author) / Pei, Yu-long (author)


    Publication date :

    2015-05-15


    Size :

    72015-01-01 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown



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