Model-Based Estimation of 3D Human Motion
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We propose a method to estimate the motion of a person filmed by two or more fixed cameras. The novelty of our technique is its ability to cope with fast movements, self-occlusions and noisy images. Our algorithms are based on the latest works on calibration and image segmentation developed in our lab. We compare the projections of a 3D model of a person on the images to the detected silhouettes of the person, and create forces that will move the 3D model towards the final estimation of the real pose. We developed a fast algorithm that computes the motion of the articulated 3D model. We show that our results are good, even if the cameras are not synchronized.