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Proceedings Paper

Implementation of a cascaded HOG-based pedestrian detector
Author(s): Christopher Reale; Prudhvi Gurram; Shuowen Hu; Alex Chan
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Paper Abstract

In this paper, we present our implementation of a cascaded Histogram of Oriented Gradient (HOG) based pedestrian detector. Most human detection algorithms can be implemented as a cascade of classifiers to decrease computation time while maintaining approximately the same performance. Although cascaded versions of Dalal and Triggs's HOG detector already exist, we aim to provide a more detailed explanation of an implementation than is currently available. We also use Asymmetric Boosting instead of Adaboost to train the cascade stages. We show that this reduces the number of weak classifiers needed per stage. We present the results of our detector on the INRIA pedestrian detection dataset and compare them to Dalal and Triggs's results.

Paper Details

Date Published: 20 May 2013
PDF: 9 pages
Proc. SPIE 8744, Automatic Target Recognition XXIII, 874403 (20 May 2013); doi: 10.1117/12.2015331
Show Author Affiliations
Christopher Reale, Univ. of Maryland, College Park (United States)
U.S. Army Research Lab. (United States)
Prudhvi Gurram, U.S. Army Research Lab. (United States)
Shuowen Hu, U.S. Army Research Lab. (United States)
Alex Chan, U.S. Army Research Lab. (United States)


Published in SPIE Proceedings Vol. 8744:
Automatic Target Recognition XXIII
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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