By Topic

Efficient scan-window based object detection using GPGPU

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Li Zhang ; Inst. of Robot. & Intell. Syst., Southern California Univ., California, MD ; Nevatia, R.

We describe an efficient design for scan-window based object detectors using a general purpose graphics hardware computing (GPGPU) framework. While the design is particularly applied to built a pedestrian detector that uses histogram of oriented gradient (HOG) features and the support vector machine (SVM) classifiers, the methodology we use is generic and can be applied to other objects, using different features and classifiers. The GPGPU paradigm is utilized for feature extraction and classification, so that the scan windows can be processed in parallel. We further propose to precompute and cache all the histograms in advance, instead of using integral images, which greatly lowers the computation cost. A multi-scale reduce strategy is employed to save expensive CPU-GPU data transfers. Experimental results show that our implementation achieves a more-than-ten-times speed up with no loss on detection rates.

Published in:

Computer Vision and Pattern Recognition Workshops, 2008. CVPRW '08. IEEE Computer Society Conference on

Date of Conference:

23-28 June 2008