Robust tracking of nonrigid objects using techniques of inverse component uncertainty factorization subspace constraints optical flow

Recently robust tracking of nonrigid objects is becoming a more and more interesting and important research topic in computer vision research community. However the traditional methods of optical flow estimation have a number of problems, such as huge computation cost for the inverse of time-varying Hessian matrix estimation, aperture phenomena for the points with 1D or little texture, drift phenomena with long sequence and hard to estimate the points with depth discontinuity. A novel algorithm namely inverse component uncertainty factorization subspace constraints optical flow based tracking is proposed in this paper, which resolves the above problems and achieves fast, robust and precise tracking. The proposed algorithm has been evaluated by both the standard test sequence and the consumer USB camera recorded sequence. The potential applications vary from articulated automation, structure from motion, computer surveillance to human-computer interaction.