Accurate optical flow computation under non-uniform brightness variations

  • Authors:

  • Chin-Hung Teng;Shang-Hong Lai;Yung-Sheng Chen;Wen-Hsing Hsu

  • Affiliations:

  • Department of Electrical Engineering, National Tsing Hua University, Hsinchu 300, Taiwan, ROC;Department of Computer Science, National Tsing Hua University, Hsinchu 300, Taiwan, ROC;Department of Electrical Engineering, Yuan Ze University, Chung-Li 320, Taiwan, ROC;Department of Electrical Engineering, National Tsing Hua University, Hsinchu 300, Taiwan, ROC and Institute of Information Science, Academia Sinica, Taipei 115, Taiwan, ROC

  • Venue:
  • Computer Vision and Image Understanding
  • Year:
  • 2005

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Abstract

In this paper, we present a very accurate algorithm for computing optical flow with non-uniform brightness variations. The proposed algorithm is based on a generalized dynamic image model (GDIM) in conjunction with a regularization framework to cope with the problem of non-uniform brightness variations. To alleviate flow constraint errors due to image aliasing and noise, we employ a reweighted least-squares method to suppress unreliable flow constraints, thus leading to robust estimation of optical flow. In addition, a dynamic smoothness adjustment scheme is proposed to efficiently suppress the smoothness constraint in the vicinity of the motion and brightness variation discontinuities, thereby preserving motion boundaries. We also employ a constraint refinement scheme, which aims at reducing the approximation errors in the first-order differential flow equation, to refine the optical flow estimation especially for large image motions. To efficiently minimize the resulting energy function for optical flow computation, we utilize an incomplete Cholesky preconditioned conjugate gradient algorithm to solve the large linear system. Experimental results on some synthetic and real image sequences show that the proposed algorithm compares favorably to most existing techniques reported in literature in terms of accuracy in optical flow computation with 100% density.