Implementation of a cascaded HOG-based pedestrian detector

Christopher Reale; Prudhvi Gurram; Shuowen Hu; Alex Chan

doi:10.1117/12.2015331

20 May 2013 Implementation of a cascaded HOG-based pedestrian detector

Christopher Reale, Prudhvi Gurram, Shuowen Hu, Alex Chan

Proceedings Volume 8744, Automatic Target Recognition XXIII; 874403 (2013) https://doi.org/10.1117/12.2015331
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

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.

Citation Download Citation

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); https://doi.org/10.1117/12.2015331

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available