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Illumination Robust Optical Flow Model Based on Histogram of Oriented Gradients

  • Conference paper
Pattern Recognition (GCPR 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8142))

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Abstract

The brightness constancy assumption has widely been used in variational optical flow approaches as their basic foundation. Unfortunately, this assumption does not hold when illumination changes or for objects that move into a part of the scene with different brightness conditions. This paper proposes a variation of the L1-norm dual total variational (TV-L1) optical flow model with a new illumination-robust data term defined from the histogram of oriented gradients computed from two consecutive frames. In addition, a weighted non-local term is utilized for denoising the resulting flow field. Experiments with complex textured images belonging to different scenarios show results comparable to state-of-the-art optical flow models, although being significantly more robust to illumination changes.

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Rashwan, H.A., Mohamed, M.A., García, M.A., Mertsching, B., Puig, D. (2013). Illumination Robust Optical Flow Model Based on Histogram of Oriented Gradients. In: Weickert, J., Hein, M., Schiele, B. (eds) Pattern Recognition. GCPR 2013. Lecture Notes in Computer Science, vol 8142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40602-7_38

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  • DOI: https://doi.org/10.1007/978-3-642-40602-7_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40601-0

  • Online ISBN: 978-3-642-40602-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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