VideoSticker: A Tool for Active Viewing and Visual Note-taking from Videos
Yining Cao, Hariharan Subramonyam, Eytan Adar
Video is an effective medium for knowledge communication and learning. Yet active viewing and note-taking from videos remain a challenge. Specifically, during note-taking, viewers find it difficult to extract essential information such as representation, composition, motion, and interactions of graphical objects and narration. Current approaches rely on creating static screenshots, manual clipping, manual annotation and transcription. This is often done by repeatedly pausing and rewinding the video, thus disrupting the viewing experience. We propose VideoSticker, a tool designed to support visual note-taking by extracting expressive content and narratives from videos as 'object stickers.' VideoSticker implements automated object detection and tracking, linking objects to the transcript, and supporting rapid extraction of stickers across space, time, and events of interest. VideoSticker's two-pass approach allows viewers to capture high-level information uninterrupted and later extract specific details. We demonstrate the usability of VideoSticker for a variety of videos and note-taking needs.
Pre-print: PDF, (50 MB)
Yining Cao, Hariharan Subramonyam, and Eytan Adar. 2022. VideoSticker: A Tool for Active Viewing and Visual Note-taking from Videos. In 27th International Conference on Intelligent User Interfaces (IUI '22). Association for Computing Machinery, New York, NY, USA, 672-690. https://doi.org/10.1145/3490099.3511132