Articulated Human Detection with Flexible Mixtures of Parts

We describe a method for articulated human detection and human pose estimation in static images based on a new representation of deformable part models. Rather than modeling articulation using a family of warped (rotated and foreshortened) templates, we use a mixture of small, nonoriented parts. We...

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Bibliographic Details
Published in:	IEEE transactions on pattern analysis and machine intelligence Vol. 35; no. 12; pp. 2878 - 2890
Main Authors:	Yi Yang, Ramanan, Deva
Format:	Journal Article
Language:	English
Published:	Los Alamitos, CA IEEE 01.12.2013 IEEE Computer Society
Subjects:	Algorithms Applied sciences articulated shapes Artificial intelligence Computational modeling Computer science; control theory; systems Data processing. List processing. Character string processing Deformable models deformable part models Exact sciences and technology Human factors Humans Memory organisation. Data processing Models, Theoretical object detection Object segmentation Pattern recognition. Digital image processing. Computational geometry Pose estimation Reproducibility of Results Shape analysis Software Computer vision Motion estimation Pose estimation articulated shapes Spatial analysis Mixture theory Tree structure Modeling Posture Experimental result Object detection Dynamic programming deformable part models Localization Cooccurrence analysis Mechanical deformation
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:	Get full text
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