On the improvement of anthropometry and pose estimation from a single uncalibrated image
We developed a technique that allows semiautomatic estimation of anthropometry and pose from a single image. However, estimation was limited to a class of images for which an adequate number of human body segments were almost parallel to the image plane. We present a generalization of that estimation algorithm that exploits pairwise geometric relationships of body segments to allow estimation from a broader class of images. In addition, we refine our search space by constructing a fully populated discrete hyper-ellipsoid of stick human body models (SMs) in order to capture the variance of the statistical anthropometric information. As a result, a better initial estimate can be computed by our algorithm and thus the number of iterations needed during minimization are reduced by tenfold. We present our results over a variety of images to demonstrate the broad coverage of our algorithm.