Efficient HOG human detection

While Histograms of Oriented Gradients (HOG) plus Support Vector Machine (SVM) (HOG+SVM) is the most successful human detection algorithm, it is time-consuming. This paper proposes two ways to deal with this problem. One way is to reuse the features in blocks to construct the HOG features for inters...

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Bibliographic Details
Published in:Signal processing Vol. 91; no. 4; pp. 773 - 781
Main Authors: Pang, Yanwei, Yuan, Yuan, Li, Xuelong, Pan, Jing
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.04.2011
Elsevier
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ISSN:0165-1684, 1872-7557
Online Access:Get full text
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Summary:While Histograms of Oriented Gradients (HOG) plus Support Vector Machine (SVM) (HOG+SVM) is the most successful human detection algorithm, it is time-consuming. This paper proposes two ways to deal with this problem. One way is to reuse the features in blocks to construct the HOG features for intersecting detection windows. Another way is to utilize sub-cell based interpolation to efficiently compute the HOG features for each block. The combination of the two ways results in significant increase in detecting humans—more than five times better. To evaluate the proposed method, we have established a top-view human database. Experimental results on the top-view database and the well-known INRIA data set have demonstrated the effectiveness and efficiency of the proposed method.
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ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2010.08.010