Optical Flow Estimation Based on SVD Covariance Weighted Techniques
一种基于SVD协方差加权技术的光流估计算法

A novel algorithm namely covariance weighted SVD based optical flow estimation is proposed in this paper, which achieves more robust and precise tracking result comparing to the traditional L-K tracker. Covariance weighted is used to transform the problem from a hyper-ellipse space to a hyper-sphere space. SVD is correspondingly performed to involve the subspace constraints. The proposed algorithm has been evaluated by the standard test sequence. The potential applications vary from articulated automation, structure from motion, computer surveillance to human-computer interaction.