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20 May 2013 Implementation of a cascaded HOG-based pedestrian detector
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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.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Christopher Reale, Prudhvi Gurram, Shuowen Hu, and Alex Chan "Implementation of a cascaded HOG-based pedestrian detector", Proc. SPIE 8744, Automatic Target Recognition XXIII, 874403 (20 May 2013);

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