Publication
Swifter: Improved Online Video Scrubbing
AbstractOnline streaming video systems have become extremely popular, yet navigating to target scenes of interest can be a challenge. While recent techniques have been introduced to enable real-time seeking, they break down for large videos, where scrubbing the timeline causes video frames to skip and flash too quickly to be comprehendible. We present Swifter, a new video scrubbing technique that displays a grid of pre-cached thumbnails during scrubbing actions. In a series of studies, we first investigate possible design variations of the Swifter technique, and the impact of those variations on its performance. Guided by these results we compare an implementation of Swifter to the previously published Swift technique, in addition to the approaches utilized by YouTube and Netfilx. Our study finds that Swifter significantly outperforms each of these techniques in a scene locating task, by a factor of up to 48%.
Download publicationRelated Resources
See what’s new.
2022
T-Domino: Exploring Multiple Criteria with Quality-Diversity and the Tournament Dominance ObjectiveA new ranking system for Multi-Criteria Exploration (MCX) that uses…
2023
Immersive Sampling: Exploring Sampling for Future Creative Practices in Media-Rich, Immersive SpacesSupporting creative practitioners in collecting materials beyond the…
2019
A First-Order Logic Formalization of the Industrial Ontologies Foundry Signature Using Basic Formal OntologyBasic Formal Ontology (BFO) is a top-level ontology used in hundreds…
2009
Mathematical modeling and synthetic biologySynthetic biology is an engineering discipline that builds on our…
Get in touch
Something pique your interest? Get in touch if you’d like to learn more about Autodesk Research, our projects, people, and potential collaboration opportunities.
Contact us