Holistic human pose estimation with regression forests
Amann C, Navab N, Ilic S, Belagiannis V (2014)
Publication Type: Conference contribution
Publication year: 2014
Journal
Publisher: Springer Verlag
Book Volume: 8563 LNCS
Pages Range: 20-30
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: ESP
ISBN: 9783319088488
DOI: 10.1007/978-3-319-08849-5_3
Abstract
In this work, we address the problem of human pose estimation in still images by proposing a holistic model for learning the appearance of the human body from image patches. These patches, which are randomly chosen, are used for extracting features and training a regression forest. During training, a mapping between image features and human poses, defined by joint offsets, is learned; while during prediction, the body joints are estimated with an efficient mode-seeking algorithm. In comparison to other holistic approaches, we can recover body poses from occlusion or noisy data. We demonstrate the power of our method in two publicly available datasets and propose a third one. Finally, we achieve state-of-the-art results in comparison to other approaches. © 2014 Springer International Publishing.
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Germany (DE)How to cite
APA:
Amann, C., Navab, N., Ilic, S., & Belagiannis, V. (2014). Holistic human pose estimation with regression forests. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 20-30). ESP: Springer Verlag.
MLA:
Amann, Christian, et al. "Holistic human pose estimation with regression forests." Proceedings of the 8th International Conference on Articulated Motion and Deformable Objects, AMDO 2014, ESP Springer Verlag, 2014. 20-30.
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