Real-Time Human Pose Tracking from Range Data
Venue
Proceedings of the European Conference on Computer Vision (ECCV) (2012)
Publication Year
2012
Authors
Varun Ganapathi, Christian Plagemann, Daphne Koller, Sebastian Thrun
BibTeX
Abstract
Tracking human pose in real-time is a difficult problem with many interesting
applications. Existing solutions suffer from a variety of problems, especially when
confronted with unusual human poses. In this paper, we derive an algorithm for
tracking human pose in real-time from depth sequences based on MAP inference in a
probabilistic temporal model. The key idea is to extend the iterative closest
points (ICP) objective by modeling the constraint that the observed subject cannot
enter free space, the area of space in front of the true range measurements. Our
primary contribution is an extension to the articulated ICP algorithm that can
efficiently enforce this constraint. Our experiments show that including this term
improves tracking accuracy significantly. The resulting filter runs at 125 frames
per second using a single desktop CPU core. We provide extensive experimental
results on challenging real-world data, which show that the algorithm outperforms
the previous state-of the-art trackers both in computational efficiency and
accuracy.
