Abstract
Vision-based motion perception builds primarily on the concept of optical flow. Modern optical flow approaches suffer from several shortcomings, especially in real, non-ideal scenarios such as traffic scenes. Non-constant illumination conditions in consecutive frames of the input image sequence are among these shortcomings. We propose and evaluate the application of intrinsically illumination-invariant census transforms within a dense state-of-the-art variational optical flow computation scheme. Our technique improves robustness against illumination changes, caused either by altering physical illumination or camera parameter adjustments. Since census signatures can be implemented quite efficiently, the resulting optical flow fields can be computed in real-time.
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Müller, T., Rabe, C., Rannacher, J., Franke, U., Mester, R. (2011). Illumination-Robust Dense Optical Flow Using Census Signatures. In: Mester, R., Felsberg, M. (eds) Pattern Recognition. DAGM 2011. Lecture Notes in Computer Science, vol 6835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23123-0_24
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DOI: https://doi.org/10.1007/978-3-642-23123-0_24
Publisher Name: Springer, Berlin, Heidelberg
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