Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectors
Top Cited Papers
- 1 January 2005
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 1 (15505499) , 90-97 Vol. 1
- https://doi.org/10.1109/iccv.2005.74
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.Keywords
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