Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking People by Learning Their Appearance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
Patch-based pose inference with a mixture of density estimators
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Efficient upper body pose estimation from a single image or a sequence
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Boundary fragment matching and articulated pose under occlusion
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
A local basis representation for estimating human pose from cluttered images
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
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Detection of humans and estimation of their 2D poses from a single image are challenging tasks. This is especially true when part of the observation is occluded. However, given a limited class of movements, poses can be recovered given the visible body-parts. To this end, we propose a novel template representation where the body is divided into five body-parts. Given a match, we not only estimate the joints in the body-part, but all joints in the body. Quantitative evaluation on a HumanEva walking sequence shows mean 2D errors of approximately 27.5 pixels. For simulated occlusion of the head and arms, similar results are obtained while occlusion of the legs increases this error by 6 pixels.