Learning Effective Human Pose Estimation from Inaccurate Annotation

10 years 8 months ago
Learning Effective Human Pose Estimation from Inaccurate Annotation
The task of 2-D articulated human pose estimation in natural images is extremely challenging due to the high level of variation in human appearance. These variations arise from different clothing, anatomy, imaging conditions and the large number of poses it is possible for a human body to take. Recent work has shown state-of-the-art results by partitioning the pose space and using strong nonlinear classifiers such that the pose dependence and multi-modal nature of body part appearance can be captured. We propose to extend these methods to handle much larger quantities of training data, an order of magnitude larger than current datasets, and show how to utilize Amazon Mechanical Turk and a latent annotation update scheme to achieve high quality annotations at low cost. We demonstrate a significant increase in pose estimation accuracy, while simultaneously reducing computational expense by a factor of 10, and contribute a dataset of 10,000 highly articulated poses.
Sam Johnson, Mark Everingham
Added 28 Mar 2011
Updated 29 Apr 2011
Type Journal
Year 2011
Where CVPR
Authors Sam Johnson, Mark Everingham
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