Abstract
Human pose reconstruction using depth images has received much attention for human-centric applications. Body-part labeling at pixel-level has shown to be efficient for human pose reconstruction. This paper presents an accurate human pose reconstruction method from a single depth image by combining body-part labeling and nearest pose-matching techniques. New pixel-level depth difference and local curvature-encoding features are introduced to provide more contextual depth information for pixel-level body-part labeling. To reduce the misclassification error, inspired by pose-matching techniques, a corrective step is also proposed. The method extracts depth region proposals from a reference pose and finds the best match using PCT coefficients to correct uncertain labels. Tests on a set of synthetic and natural depth poses showed improved accuracy of body-part labeling compared to the state-of-the-art methods. In addition, in comparison with the previous methods and the Kinect camera, an improved accuracy for human range of motion measurement was obtained .
Published Version
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