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.
Josh Harguess, Diego Marez, and Nancy Ronquillo, "An investigation into strategies to improve optical flow on degraded data," Proc. SPIE 10645, Geospatial Informatics, Motion Imagery, and Network Analytics VIII, 106450F (Presented at SPIE Defense + Security: April 16, 2018; Published: 8 May 2018); https://doi.org/10.1117/12.2305295.
Conference Presentations are recordings of oral presentations given at SPIE conferences and published as part of the proceedings. They include the speaker's narration with video of the slides and animations. Most include full-text papers. Interactive, searchable transcripts and closed captioning are now available for 2018 presentations, with transcripts for prior recordings added daily.
Search our growing collection of more than 16,000 conference presentations, including many plenaries and keynotes.