Print Email Facebook Twitter How do deep neural networks perform optical flow estimation? Title How do deep neural networks perform optical flow estimation?: A neuropsychology-inspired study Author de Jong, David (TU Delft Aerospace Engineering) Contributor de Croon, G.C.H.E. (mentor) Paredes-Vallés, Federico (graduation committee) Degree granting institution Delft University of Technology Programme Aerospace Engineering Date 2020-04-14 Abstract End-to-end trained Convolutional Neural Networks have led to a breakthrough in optical flow estimation. The most recent advances focus on improving the optical flow estimation by improving the architecture and setting a new benchmark on the publicly available MPI-Sintel dataset. Instead, in this article, we investigate how deep neural networks estimate optical flow. By obtaining an understanding of how these networks function, more can be said about the behavior of these networks in unexpected scenarios and how the architecture and training data can be improved to obtain a better performance. For our investigation, we use a filter identification method that has played a major role in uncovering the motion filters present in animal brains in neuropsychological research. The method shows that the filters in deep neural networks are sensitive to a variety of motion patterns. Not only do we find translation filters, as demonstrated in animal brains, but thanks to the easier measurements in artificial neural networks, we even unveil dilation, rotation and occlusion filters. Furthermore, we find similarities in the refinement part of the network and the perceptual filling-in process which occurs in the mammal primary visual cortex. Subject optical flowGabor filtersconvolutional neural networksneurospychology To reference this document use: http://resolver.tudelft.nl/uuid:64d40dfc-d852-4688-b8f4-af37f3e9704c Embargo date 2021-04-14 Part of collection Student theses Document type master thesis Rights © 2020 David de Jong Files PDF finalthesis_DaviddeJong.pdf 16.49 MB Close viewer /islandora/object/uuid:64d40dfc-d852-4688-b8f4-af37f3e9704c/datastream/OBJ/view