Illumination robust optical flow estimation by illumination-chromaticity decoupling

In this paper, a novel optical flow algorithm which is robust to illumination variation is proposed. HSL color space is adopted to decouple illumination and chromaticity information. The chromaticity component is normalized by chroma and transformed to the cartesian coordinate. Then, the decoupled distance is defined using both illumination and chromaticity. Cost function for the optical flow is formulated using l1 norm of the decoupled distance with Huber norm regularization term. The cost function is efficiently minimized by utilizing Legendre-Fenchel transform. Optical flow field is further refined via weighted median filter whose weight is also based on the decoupled distance. Experimental results show that the proposed method works robustly even in the presence of severe illumination variation.

[1]  Joachim Weickert,et al.  Illumination-Robust Variational Optical Flow with Photometric Invariants , 2007, DAGM-Symposium.

[2]  Daniel Cremers,et al.  An Improved Algorithm for TV-L 1 Optical Flow , 2009, Statistical and Geometrical Approaches to Visual Motion Analysis.

[3]  Yasuyuki Matsushita,et al.  Motion detail preserving optical flow estimation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Michael J. Black,et al.  A Naturalistic Open Source Movie for Optical Flow Evaluation , 2012, ECCV.

[5]  H. Fleyeh,et al.  Color detection and segmentation for road and traffic signs , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[6]  Richard Szeliski,et al.  A Database and Evaluation Methodology for Optical Flow , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[7]  Antonin Chambolle,et al.  A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging , 2011, Journal of Mathematical Imaging and Vision.

[8]  David A. Clausi,et al.  A Decoupled Approach to Illumination-Robust Optical Flow Estimation , 2013, IEEE Transactions on Image Processing.

[9]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[10]  Michael J. Black,et al.  A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them , 2013, International Journal of Computer Vision.

[11]  Michael J. Black,et al.  Secrets of optical flow estimation and their principles , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Daniel Cremers,et al.  Anisotropic Huber-L1 Optical Flow , 2009, BMVC.

[13]  Mads Nielsen,et al.  TV-L1 Optical Flow for Vector Valued Images , 2011, EMMCVPR.

[14]  Andrew J. Davison,et al.  DTAM: Dense tracking and mapping in real-time , 2011, 2011 International Conference on Computer Vision.

[15]  Lourdes Agapito,et al.  A Variational Approach to Video Registration with Subspace Constraints , 2013, International Journal of Computer Vision.

[16]  S. Osher,et al.  A new median formula with applications to PDE based denoising , 2009 .

[17]  Reinhard Koch,et al.  A Color Similarity Measure for Robust Shadow Removal in Real Time , 2003, VMV.

[18]  Horst Bischof,et al.  A Duality Based Approach for Realtime TV-L1 Optical Flow , 2007, DAGM-Symposium.