Monocular pose capture with a depth camera using a sums-of-Gaussians body model

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). © 2013 Springer-Verlag.