Abstract
This paper presents a flexible and scalable approach to the parallelization of the computation of optical flow. This approach is based on data parallel distribution. Images are divided into several subimages processed by a software pipeline while respecting dependencies between computation stages. The parallelization has been implemented in three different infrastructures: shared, distributed memory, and hybrid to show its conceptual flexibility and scalability. A significant improvement in performance was obtained in all three cases. These versions have been used to compute the optical flow of video sequences taken in adverse conditions, with a moving camera and natural-light conditions, on board a conventional vehicle traveling on public roads. The parallelization adopted has been developed from the analysis of dependencies presented by the well-known Lucas-Kanade algorithm, using a sequential version developed at the University of Porto as the starting point.
Highlights
Optical flow is an image analysis technique used to detect motion in video sequences
A preliminary version of this paper [54] presents a parallelization of the Lucas-Kanade algorithm applied to the computation of optical flow on video sequences taken from a moving vehicle in real traffic
11 Conclusion This paper addresses the parallelization of optical flow calculation
Summary
Optical flow is an image analysis technique used to detect motion in video sequences. A preliminary version of this paper [54] presents a parallelization of the Lucas-Kanade algorithm applied to the computation of optical flow on video sequences taken from a moving vehicle in real traffic. The method described is based on dividing the Lucas-Kanade algorithm into several tasks that must be processed sequentially, each one using a different number of images from the video sequence.
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