Probabilistic human pose recovery from 2D images

Image based human pose recovery has many applications in different industries such as games, entertainment, physiological rehabilitation and biometrics. This paper presents a new pose estimation algorithm from monocular images based on a nonlinear mapping of human silhouettes, coded using a collection of local image moments, to the pose space using a mixture of Neural Networks (NN) regressors. All parameters are estimated automatically. Experiments and comparative results show a superior performance of the proposed method.

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