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Open AccessArticle

Optimal Filter Estimation for Lucas-Kanade Optical Flow

Computer Science Department, Lucian Blaga University of Sibiu, B-dul Victoriei 10, 550024 Sibiu, Romania
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Sensors 2012, 12(9), 12694-12709; https://doi.org/10.3390/s120912694
Received: 7 June 2012 / Revised: 3 September 2012 / Accepted: 4 September 2012 / Published: 17 September 2012
(This article belongs to the Section Physical Sensors)
Optical flow algorithms offer a way to estimate motion from a sequence of images. The computation of optical flow plays a key-role in several computer vision applications, including motion detection and segmentation, frame interpolation, three-dimensional scene reconstruction, robot navigation and video compression. In the case of gradient based optical flow implementation, the pre-filtering step plays a vital role, not only for accurate computation of optical flow, but also for the improvement of performance. Generally, in optical flow computation, filtering is used at the initial level on original input images and afterwards, the images are resized. In this paper, we propose an image filtering approach as a pre-processing step for the Lucas-Kanade pyramidal optical flow algorithm. Based on a study of different types of filtering methods and applied on the Iterative Refined Lucas-Kanade, we have concluded on the best filtering practice. As the Gaussian smoothing filter was selected, an empirical approach for the Gaussian variance estimation was introduced. Tested on the Middlebury image sequences, a correlation between the image intensity value and the standard deviation value of the Gaussian function was established. Finally, we have found that our selection method offers a better performance for the Lucas-Kanade optical flow algorithm. View Full-Text
Keywords: optical flow; Lucas-Kanade; Gaussian filtering; optimal filtering optical flow; Lucas-Kanade; Gaussian filtering; optimal filtering
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Sharmin, N.; Brad, R. Optimal Filter Estimation for Lucas-Kanade Optical Flow. Sensors 2012, 12, 12694-12709.

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