GPU TV-L1 Optical Flow

Brian Hrolenok, Chris McClanahan
School of Interactive Computing, College of Computing at Georgia Tech.
Georgia Tech., 2011


   title={GPU TV-L1 Optical Flow},

   author={Hrolenok, Brian and McClanahan, Chris},



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Determining optical flow, the pattern of apparent motion of objects caused by the relative motion between observer and objects in the scene, is a fundamental problem in computer vision. Given two images, goal is to compute the 2D motion field – a projection of 3D velocities of surface points onto the imaging surface. Optical flow can be used in a wide range of higher level computer vision tasks, from object tracking and robot navigation to motion estimation and image stabilization. There is a real need for shortening the required computational time of optical flow for use in practical applications such as robotics motion analysis and security systems. With the advances in utilizing GPUs for general computation, its become feasible to use more accurate (but computationally expensive) optical flow algorithms for these practical applications. With that in mind, we propose to implement an improved L1-norm based total-variation optical flow (TVL1) on the GPU.
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