Abstract
We propose a real-time capable method for human pose estimation from depth and color images that does not need any pre-trained pose classifiers. The pose estimation focuses on the upper body, as it is the relevant part for a subsequent gesture and posture recognition and therefore the basis for a real human-machine-interaction. Using a graph-based representation of the 3D point cloud, we compute geodesic distances between body parts. The geodesic distances are independent of pose and allow the robust determination of anatomical landmarks which serve as input to a skeleton fitting process using inverse kinematics. In case of degenerated graphs, landmarks are tracked locally with a meanshift algorithm based on skin color probability.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02895-8_64
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Handrich, S., Al-Hamadi, A. (2013). Upper-Body Pose Estimation Using Geodesic Distances and Skin-Color. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2013. Lecture Notes in Computer Science, vol 8192. Springer, Cham. https://doi.org/10.1007/978-3-319-02895-8_14
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DOI: https://doi.org/10.1007/978-3-319-02895-8_14
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