NDLI: Pedestrian detection by modeling local convex shape features

Content Provider	IEEE Xplore Digital Library
Author	Jungme Park Yun Luo Haoxing Wang Murphey, Y.L.
Copyright Year	2008
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Michigan-Dearborn, Dearborn, MI (Jungme Park; Haoxing Wang; Murphey, Y.L.) \|\| Driver Assistance Group, Robert Bosch, Plymouth, MI (Yun Luo)
Abstract	This paper presents a pedestrian model built collectively on a group of strong local convex shape descriptors. The pedestrian model captures the most important features of a pedestrian: head, body contour, arms, legs and crotch, and is robust to variances in appearances and partial occlusions. For an image set of 2571 pedestrians and 4369 car and background images, the pedestrian recognition system, which was built upon the proposed pedestrian model, gave a recognition rate of 98.8% with a false positive rate of 1.56%. Furthermore, the pedestrian recognition requires a very small set of prototypes of pedestrians and non-pedestrians.
Starting Page	1
Ending Page	4
File Size	294560
Page Count	4
File Format	PDF
ISBN	9781424421749
ISSN	10514651
DOI	10.1109/ICPR.2008.4761708
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape Image edge detection Image recognition Head Arm Leg Robustness Prototypes Principal component analysis System testing
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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