Progressive Search Space Reduction for Human Pose Estimation
Vittorio Ferrari, Manuel Marin-Jimenez and Andrew Zisserman
In: CVPR, 24-26 June 2008, Anchorage, Alaska, USA.
The objective of this paper is to estimate 2D human pose as a spatial
configuration of body parts in TV and movie video shots. Such
video material is uncontrolled and extremely challenging.
We propose an approach that progressively reduces the search space for body parts, to greatly improve the chances that pose estimation will succeed. This involves two contributions: (i) a generic detector using a weak model of pose to substantially reduce the full pose search space; and (ii) employing `grabcut' initialized on detected regions proposed by the weak model, to further prune the search space. Moreover, we also propose
(iii) an integrated spatio-temporal model covering multiple frames to
refine pose estimates from individual frames, with inference using
The method is fully automatic and self-initializing, and explains the
spatio-temporal volume covered by a person moving in a shot, by
soft-labeling every pixel as belonging to a particular body part or to
the background. We demonstrate upper-body pose estimation by an extensive evaluation over 70000 frames from four episodes of the TV series `Buffy the vampire slayer', and present an application to full-body action recognition on the Weizmann dataset.
|EPrint Type:||Conference or Workshop Item (Poster)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Deposited By:||Vittorio Ferrari|
|Deposited On:||24 March 2009|