Abstract
In this paper, we address the problem of 3D human body pose estimation from depth images acquired by a stereo camera. Compared to the Kinect sensor, stereo cameras work outdoors having a much higher operational range, but produce noisier data. In order to deal with such data, we propose a framework for 3D human pose estimation that relies on random forests. The first contribution is a novel grid-based shape descriptor robust to noisy stereo data that can be used by any classifier. The second contribution is a two step classification procedure, first classifying the body orientation, then proceeding with determining the full 3D pose within this orientation cluster. To validate our method, we introduce a dataset recorded with a stereo camera synchronized with an optical motion capture system that provides ground truth human body poses.
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Lallemand, J., Szczot, M., Ilic, S. (2014). Human Pose Estimation in Stereo Images. In: Perales, F.J., Santos-Victor, J. (eds) Articulated Motion and Deformable Objects. AMDO 2014. Lecture Notes in Computer Science, vol 8563. Springer, Cham. https://doi.org/10.1007/978-3-319-08849-5_2
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DOI: https://doi.org/10.1007/978-3-319-08849-5_2
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