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Conference Paper
Robust Geodesic Skeleton Estimation from Body Single Depth
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- Authors
- Jaehwan Kim, Howon Kim
- Issue Date
- 2018-09
- Citation
- International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS) 2018 (LNCS 11182), pp.342-353
- Publisher
- Springer
- Language
- English
- Type
- Conference Paper
- Abstract
- In this paper, we introduce a novel and robust body pose estimation method with single depth image, whereby it is possible to provide the skeletal configuration of the body with significant accuracy even in the condition of severe body deformations. In order for the precise identification, we propose a novel feature descriptor based on a geodesic path over the body surface by accumulating sequence of characters correspond to the path vectors along body deformations, which is referred to as GPS (Geodesic Path Sequence). We also incorporate the length of each GPS into a joint entropy-based objective function representing both class and structural information, instead of the typical objective considering only class labels in training the random forest classifier. Furthermore, we exploit a skeleton matching method based on the geodesic extrema of the body, which enhances more robustness to joints misidentification. The proposed solutions yield more spatially accurate predictions for the body parts and skeletal joints. Numerical and visual experiments with our generated data confirm the usefulness of the method.
- KSP Keywords
- Body parts, Body surface, Feature descriptor, Geodesic path, Objective function, Pose estimation, Random Forest Classifier, Single Depth Image, Skeletal joints, Structural information, Visual experiments