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Pedestrian Detection by Boosting Neural Networks
Jia Hui-Xing * #,Zhang Yu-Jin
Tsinghua University
*Correspondence author
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Funding: 国家自然科学基金,教育部博士点基金（No.60573148,20060003102）
Opened online:17 December 2008
Accepted by: none
Citation: Jia Hui-Xing,Zhang Yu-Jin.Pedestrian Detection by Boosting Neural Networks[OL]. [17 December 2008] http://en.paper.edu.cn/en_releasepaper/content/26680
 
 
In this paper, a fast pedestrian detection system by boosting neural network classifiers is built. The object to be detected is represented by a collection of blocks. For each block, the histogram of orientated gradients feature is extracted and a neural network classifier is built as weak hypothesis. Then these hypotheses are selected sequentially by Gentle Adaboost, and the cascade structure is used to speedup the detector. Compared to global linear SVM classifiers, the new method gets better performance on the INRIA pedestrian detection database at a much faster speed.
Keywords:Pedestrian Detection;Gentle Adaboost;Neural Network;Histograms of Oriented Gradients
 
 
 

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