Study on the Method of Pedestrian Detection in Automobile Safety System

Abstract:

In order to improve automobile active safety performance, and reduce the traffic accidents between pedestrians and vehicles, a pedestrian detection method combined with pedestrian contour features is proposed based on the combination of the reliable Adaboost and SVM. For the requirements of fast and accurate pedestrian detection system, ten types of haar-like features are given as the coarse features firstly, and which are trained through Adaboost cascade algorithm to ensure the system with a high detection speed. Then, the hog features of strong ability to distinguish pedestrians are selected as the fine features, and the pedestrian classifier is got by using SVM of different kernels to improve the detection accuracy. It is shown that the method has a higher detection rate and achieves a better detection effect.

• Periodical: Advanced Materials Research (Volume 580)
• Pages: 118-121
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.580.118
• Online since: October 2012
• Authors: Zhong Hao Bai, Zhi Peng Ding, Qiang Yan
• Keywords: Adaboost, Automobile Safety, Haar-Like, Histograms of Oriented Gradient (HOG), Pedestrian Detection, Support Vector Machine (SVM)

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