Abstract
Optical flow is the apparent motion pattern of pixels in two consecutive images. Optical flow has many applications: navigation control of autonomous vehicles, video compression, noise suppression, and others. There are different methods to estimate the optical flow, where variational models are the most frequently used. These models state an energy model to compute the optical flow. These models may fail in presence of occlusions and illumination changes. In this work is presented a method that estimates the flow from the classical Horn-Schunk method and the incorporation of an occlusion layer that gives to the model the ability to handle occlusions. The proposed model was implemented in an Intel i7, 3.5 GHz, GPU GeForce NVIDIA-GTX-980-Ti, using a standard webcam. Using images of \(320\times 240\) pixels we reached 4 images per second, i.e. this implementation can be used in an application like an autonomous vehicle.
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Baker, S., Scharstein, D., Lewis, J., Roth, S., Black, M., Szelensky, R.: A database and evaluation methodology for optical flow. Int. J. Comput. Vis. 92, 1–31 (2011)
Ballester, C., Garrido, L., Lazcano, V., Caselles, V.: A TV-L1 optical flow method with occlusion detection. In: Pinz, A., Pock, T., Bischof, H., Leberl, F. (eds.) DAGM/OAGM 2012. LNCS, vol. 7476, pp. 31–40. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32717-9_4
Barron, J., Fleet, D., Beauchemin, S.: Performance of optical flow technics. Int. J. Comput. Vis. 12(1), 43–47 (2011)
Garamendi, J.F., Ballester, C., Garrido, L., Lazcano, V.: Joint TV-L1 optical flow and occlusion estimation. IPOL J. - Image Process. On Line, preprint (2014)
Horn, B., Schunck, B.G.: Determining optical flow. Artif. Intell. 17, 185–204 (1981)
Lazcano, V., Garrido, L., Ballester, C.: Jointly optical flow and occlusion estimation for images with large displacements. In: SciTePress (ed.) Proceedings of the 13th International Joint Conference on Computer Vision Imaging and Computer Graphics Theory and Applications, pp. 588–595. INSTICC (2018)
Sand, P., Teller, S.: Particle video: long-range motion estimation using point trajectory. Int. J. Comput. Vis. 80(1), 72 (2008)
Smirnov, M.: Optical flow estimation with CUDA. NVIDIA white papers (2012)
Xiao, J., Cheng, H., Sawhney, H., Rao, C., Isnardi, M.: Bilateral filtering-based optical flow estimation with occlusion detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 211–224. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_17
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Lazcano, V., Rivera, F. (2019). GPU Based Horn-Schunck Method to Estimate Optical Flow and Occlusion. In: Gopal, T., Watada, J. (eds) Theory and Applications of Models of Computation. TAMC 2019. Lecture Notes in Computer Science(), vol 11436. Springer, Cham. https://doi.org/10.1007/978-3-030-14812-6_26
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DOI: https://doi.org/10.1007/978-3-030-14812-6_26
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