Detection of Multiple, Partially Occluded Humans in a Single Image by Bayesian Combination of Edgelet Part Detectors

Authors:
Bo Wu;Ram Nevatia
Affiliations:
University of Southern California;University of Southern California
Venue:
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Year:
2005

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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.