NDLI: Efficient scan-window based object detection using GPGPU

Content Provider	IEEE Xplore Digital Library
Author	Li Zhang Nevatia, R.
Copyright Year	2008
Description	Author affiliation: Inst. of Robot. & Intell. Syst., Southern California Univ., California, MD (Li Zhang; Nevatia, R.)
Abstract	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.
Starting Page	1
Ending Page	7
File Size	563095
Page Count	7
File Format	PDF
ISBN	9781424423392
ISSN	21607508
DOI	10.1109/CVPRW.2008.4563097
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Graphics Support vector machines Histograms Support vector machine classification Object detection Detectors Rendering (computer graphics) Feature extraction Hardware Intelligent robots
Content Type	Text
Resource Type	Article

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