Recognizing Human Postures and Poses in Monocular Still Images
Loading...
Authors
Wachs, J.P.
Goshorn, D.
Kolsch, M.
Subjects
posture recognition
part-based detectors
pose detection
multi-class detectors
Adaboost
part-based detectors
pose detection
multi-class detectors
Adaboost
Advisors
Date of Issue
2009
Date
Publisher
Language
Abstract
In this paper, person detection with
simultaneous or subsequent human body posture
recognition is achieved using parts-based models, since
the search space for typical poses is much smaller than
the kinematics space. Posture recovery is carried out by
detecting the human body, its posture and orientation at
the same time. Since features of different human postures
can be expected to have some shared subspace against
the non-person class, detection and classification
simultaneously is tenable. Contrary to many related
efforts, we focus on postures that cannot be easily
distinguished after segmentation by their aspect ratio or
silhouette, but rather require a texture-based feature
vector. The approaches presented do not rely on explicit
models nor on labeling individual body parts. Both the
detection and classification are performed in one pass on
the image, where the score of the detection is an ensemble
of votes from parts patches.
Type
Article
Description
Series/Report No
Department
Computer Science (CS)
Organization
Modeling, Virtual Environments, and Simulation Institute (MOVES)
Identifiers
NPS Report Number
Sponsors
Funder
Format
Citation
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.