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Durham University

Computer Science


Publication details for Professor Toby Breckon

Stephenson, F., Breckon, T.P. & Katramados, I. (2019), DeGraF-Flow: Extending DeGraF Features for Accurate and Efficient Sparse-to-Dense Optical Flow Estimation, 26th IEEE International Conference on Image Processing (ICIP). Taipei, Taiwan, IEEE, Piscataway, NJ, 1277-1281.

Author(s) from Durham


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