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Massively parallel Lucas Kanade optical flow for real-time video processing applications

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Abstract

This paper deals with dense optical flow estimation from the perspective of the trade-off between quality of the estimated flow and computational cost which is required by real-world applications. We propose a fast and robust local method, denoted by eFOLKI, and describe its implementation on GPU. It leads to very high performance even on large image formats such as 4 K (3,840 × 2,160) resolution. In order to assess the interest of eFOLKI, we first present a comparative study with currently available GPU codes, including local and global methods, on a large set of data with ground truth. eFOLKI appears significantly faster while providing quite accurate and highly robust estimated flows. We then show, on four real-time video processing applications based on optical flow, that eFOLKI reaches the requirements both in terms of estimated flows quality and of processing rate.

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Acknowledgments

The authors are most grateful to Benjamin Leclaire and Yves Le Sant at ONERA/DAFE for years of fruitful collaboration.

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  1. ONERA, Chemin de la Hunière et des Joncherettes, 91123, Palaiseau, France

    Aurélien Plyer, Guy Le Besnerais & Frédéric Champagnat

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  1. Aurélien Plyer
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  3. Frédéric Champagnat
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Correspondence to Aurélien Plyer.

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Plyer, A., Le Besnerais, G. & Champagnat, F. Massively parallel Lucas Kanade optical flow for real-time video processing applications. J Real-Time Image Proc 11, 713–730 (2016). https://doi.org/10.1007/s11554-014-0423-0

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