Abstract
We present a new markerless generative approach for Human Motion Tracking using a single depth camera. It is based on a Sums of Spatial Gaussians (SoGs) representation for modeling the scene. In contrast to existing systems our approach does not require a multi-view camera setup, exemplar database or training data. The proposed system is accurate, fast and capable of tracking complex motions including 360° turns and self-occlusion of limited duration. The motivation behind our approach is that representing the depth data and a given a priori human model by a SoGs, we can construct an efficient continuously differentiable similarity measure and estimate an optimal pose for each input frame using a local optimization algorithm (Modified Gradient Ascent Linear Search, MGALS).
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Kurmankhojayev, D., Hasler, N., Theobalt, C. (2013). Monocular Pose Capture with a Depth Camera Using a Sums-of-Gaussians Body Model. In: Weickert, J., Hein, M., Schiele, B. (eds) Pattern Recognition. GCPR 2013. Lecture Notes in Computer Science, vol 8142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40602-7_44
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DOI: https://doi.org/10.1007/978-3-642-40602-7_44
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