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2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2010)
San Francisco, CA, USA
June 13, 2010 to June 18, 2010
ISBN: 978-1-4244-6984-0
pp: 755-762
Varun Ganapathi , Stanford University, Computer Science Department, Stanford, CA, USA
Christian Plagemann , Stanford University, Computer Science Department, Stanford, CA, USA
Daphne Koller , Stanford University, Computer Science Department, Stanford, CA, USA
Sebastian Thrun , Stanford University, Computer Science Department, Stanford, CA, USA
ABSTRACT
Markerless tracking of human pose is a hard yet relevant problem. In this paper, we derive an efficient filtering algorithm for tracking human pose using a stream of monocular depth images. The key idea is to combine an accurate generative model — which is achievable in this setting using programmable graphics hardware — with a discriminative model that provides data-driven evidence about body part locations. In each filter iteration, we apply a form of local model-based search that exploits the nature of the kinematic chain. As fast movements and occlusion can disrupt the local search, we utilize a set of discriminatively trained patch classifiers to detect body parts. We describe a novel algorithm for propagating this noisy evidence about body part locations up the kinematic chain using the un-scented transform. The resulting distribution of body configurations allows us to reinitialize the model-based search. We provide extensive experimental results on 28 real-world sequences using automatic ground-truth annotations from a commercial motion capture system.
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CITATION

V. Ganapathi, C. Plagemann, S. Thrun and D. Koller, "Real time motion capture using a single time-of-flight camera," 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR), San Francisco, CA, USA, 2010, pp. 755-762.
doi:10.1109/CVPR.2010.5540141
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