NDLI: Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectors

Content Provider	IEEE Xplore Digital Library
Author	Bo Wu Nevatia, R.
Copyright Year	2005
Description	Author affiliation: Inst. for Robotics & Intelligent Syst., Southern California Univ., Los Angeles, CA, USA (Bo Wu; Nevatia, R.)
Abstract	This paper proposes a method for human detection in crowded scene from static images. An individual human is modeled as an assembly of natural body parts. We introduce edgelet features, which are a new type of silhouette oriented features. Part detectors, based on these features, are learned by a boosting method. Responses of part detectors are combined to form a joint likelihood model that includes cases of multiple, possibly inter-occluded humans. The human detection problem is formulated as maximum a posteriori (MAP) estimation. We show results on a commonly used previous dataset as well as new data sets that could not be processed by earlier methods.
Sponsorship	IEEE Comput. Soc. Tech. Comm. on Pattern Anal. and Machine Intelligence
Starting Page	90
Ending Page	97
File Size	571812
Page Count	8
File Format	PDF
ISBN	076952334X
ISSN	15505499
DOI	10.1109/ICCV.2005.74
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image edge detection Humans Bayesian methods Detectors Face detection Shape Biological system modeling Legged locomotion Cameras Lighting
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors.

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