Human Limb Extraction Based on Motion Estimation Using Optical Flow and Image Registration

We propose a method for extracting human limb regions by the combination of optical flow-based motion segmentation and nonlinear optimization-based image registration. First, rotating limb regions with rough boundaries are extracted and motion parameters are estimated for an approximated model. Then the extracted region and estimated parameters are used as initial values for nonlinear optimization that minimizes residuals of two successive frames and estimates motion parameters. Combining the two steps reduces computational cost and avoids the initial state problem of optimization. According to estimated parameters, the limb region is extracted by a Bayesian classifier to obtain accurate region boundaries. Experimental results on real images are shown.