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Proceedings Paper

An investigation into strategies to improve optical flow on degraded data
Author(s): Josh Harguess; Diego Marez; Nancy Ronquillo
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Paper Abstract

Much progress has been made in recent years in almost every research area within computer vision. This has led to an increased interest in applying computer vision algorithms to real-world problems, such as robot navigation, driver-less cars, and first-person video analysis. However, in each of these real-world applications, there are still significant challenges in processing degraded data, particularly when estimating motion from a single camera, which is commonly solved using optical flow. Previous studies have shown that state-of-the-art optical flow methods fail under realistic conditions of added noise, compression artifacts, and other types of degradations. In this paper we investigate strategies to improve the robustness of optical flow to these degradations by using the degradations and data augmentations in the training and fine-tuning stages of deep learning approaches to optical flow. We test these strategies using real and simulated data and attempt to illuminate this important area of research to the community.

Paper Details

Date Published: 8 May 2018
PDF: 10 pages
Proc. SPIE 10645, Geospatial Informatics, Motion Imagery, and Network Analytics VIII, 106450F (8 May 2018); doi: 10.1117/12.2305295
Show Author Affiliations
Josh Harguess, SPAWAR Systems Ctr. Pacific (United States)
Diego Marez, SPAWAR Systems Ctr. Pacific (United States)
Nancy Ronquillo, Univ. of California, San Diego (United States)


Published in SPIE Proceedings Vol. 10645:
Geospatial Informatics, Motion Imagery, and Network Analytics VIII
Kannappan Palaniappan; Peter J. Doucette; Gunasekaran Seetharaman, Editor(s)

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