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SAMOS
2015
Springer

GPU implementation of an anisotropic Huber-L1 dense optical flow algorithm using OpenCL

4 years 7 months ago
GPU implementation of an anisotropic Huber-L1 dense optical flow algorithm using OpenCL
—Optical flow estimation aims at inferring a dense pixel-wise correspondence field between two images or video frames. It is commonly used in video processing and computer vision applications, including motion-compensated frame processing, extracting temporal features, computing stereo disparity, understanding scene context/dynamics and understanding behavior. Dense optical flow estimation is a computationally complex problem. Fortunately, a wide range of optical flow estimation algorithms are embarrassingly parallel and can efficiently be accelerated on GPUs. In this work we discuss a massively multi-threaded GPU implementation of the anisotropic HuberL1 optical flow estimation algorithm using OpenCL framework, which achieves per frame execution time speed-up factors up to almost 300×. Overall algorithm flow, GPU specific implementation details and performance results are presented.
Duygu Buyukaydin, Toygar Akgun
Added 17 Apr 2016
Updated 17 Apr 2016
Type Journal
Year 2015
Where SAMOS
Authors Duygu Buyukaydin, Toygar Akgun
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