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DeGraF-Flow : extending DeGraF features for accurate and efficient sparse-to-dense optical flow estimation.

Stephenson, F. and Breckon, T.P. and Katramados, I. (2019) 'DeGraF-Flow : extending DeGraF features for accurate and efficient sparse-to-dense optical flow estimation.', in 2019 IEEE International Conference on Image Processing (ICIP) ; proceedings. Piscataway, NJ: IEEE, pp. 1277-1281.

Abstract

Modern optical flow methods make use of salient scene feature points detected and matched within the scene as a basis for sparse-to-dense optical flow estimation. Current feature detectors however either give sparse, non uniform point clouds (resulting in flow inaccuracies) or lack the efficiency for frame-rate real-time applications. In this work we use the novel Dense Gradient Based Features (DeGraF) as the input to a sparse-to-dense optical flow scheme. This consists of three stages: 1) efficient detection of uniformly distributed Dense Gradient Based Features (DeGraF) [1]; 2) feature tracking via robust local optical flow [2]; and 3) edge preserving flow interpolation [3] to recover overall dense optical flow. The tunable density and uniformity of DeGraF features yield superior dense optical flow estimation compared to other popular feature detectors within this three stage pipeline. Furthermore, the comparable speed of feature detection also lends itself well to the aim of real-time optical flow recovery. Evaluation on established real-world benchmark datasets show test performance in an autonomous vehicle setting where DeGraF-Flow shows promising results in terms of accuracy with competitive computational efficiency among non-GPU based methods, including a marked increase in speed over the conceptually similar EpicFlow approach

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1109/ICIP.2019.8803739
Publisher statement:© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:30 April 2019
Date deposited:05 June 2019
Date of first online publication:September 2019
Date first made open access:12 November 2019

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