NDLI: Parts-based object recognition seeded by frequency-tuned saliency for child detection in active safety

Content Provider	IEEE Xplore Digital Library
Author	Cheng, S.Y. Molineros, J. Owechko, Y. Levi, D. Zhang, W.
Copyright Year	2012
Description	Author affiliation: HRL Laboratories, LLC, 3011 Malibu Canyon Road, Malibu CA 90265, USA (Cheng, S.Y.; Molineros, J.; Owechko, Y.) \|\| GM Advanced Technology Center-Israel (Levi, D.) \|\| GM Research, USA (Zhang, W.)
Abstract	This paper proposes a novel system for automatically detecting children from a color monocular back-up camera, as part of a back-up warning device in passenger vehicles. We presented the use of an attentional mechansim that focuses compute-intensive bounding-box classifiers on a subset of all possible bounding-box solutions to enable real-time performance of 248ms per frame with negligible reduction in performance. The attentional mechanism called Attention to Children which consists of a window generation and verification cascade of based on Frequency-Tuned Saliency, Variational-Optical-Flow Obstacle Detection and finally a parts-based classifier. We also presented a method of reducing much of the cascade classifier evaluations by judicious sampling of the bounding-box solution space. The result is a reduction in the number of windows evaluated down to 439 from more than 12K windows in traditional sliding window techniques, a 97% reduction in the number of windows. The verification stages leading up to the parts-based classifier further reduces the number of windows to half. Together with a parallel processing and pipelining, the final processing time was 248ms per frame.
Starting Page	1155
Ending Page	1160
File Size	2074728
Page Count	6
File Format	PDF
ISBN	9781467330640
ISSN	21530009
e-ISBN	9781467330633
e-ISBN	9781467330626
DOI	10.1109/ITSC.2012.6338883
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Detectors Humans Cameras Vehicles Optical imaging Accuracy Computer vision
Content Type	Text
Resource Type	Article

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