This paper outlines the SWAV system - Semantics-based Workflows for Automatic Video Analysis. SWAV utilises ontologies and planning as core technologies to gear the composition and execution of video processing workflows. It is tailored for users without image processing expertise who have specific goals (tasks) and restrictions on these goals but not the ability to choose appropriate video processing software to solve their goals. An evaluation on a set of ecological videos has indicated that SWAV: 1) is more time-efficient at solving video classification tasks than manual processing; 2) is more adaptable in response to changes in user requests (task restrictions and video descriptions) than modifying existing image processing programs; and 3) assists the user in selecting optimal solutions by providing recommended descriptions. © 2011 Springer-Verlag.
CITATION STYLE
Nadarajan, G., Chen-Burger, Y. H., & Fisher, R. B. (2011). SWAV: Semantics-based workflows for automatic video analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6682 LNAI, pp. 681–691). https://doi.org/10.1007/978-3-642-22000-5_70
Mendeley helps you to discover research relevant for your work.