Abstract
Robust and fast algorithms for estimating the pose of a human given an image would have a far reaching impact on many fields in and outside of computer vision. We address the problem using depth data that can be captured inexpensively using consumer depth cameras such as the Kinect sensor. To achieve robustness and speed on a small training dataset, we formulate the pose estimation task within a regression and Hough voting framework. Our approach uses random regression forests to predict joint locations from each pixel and accumulate these predictions with Hough voting. The Hough accumulator images are treated as likelihood distributions where maxima correspond to joint location hypotheses. We demonstrate our approach and compare to the state-ofthe-art on a publicly available dataset.