A Sketch-Based Approach for Detecting Common Human Actions

Authors:
Evan A. Suma;Christopher Walton Sinclair;Justin Babbs;Richard Souvenir
Affiliations:
Department of Computer Science, University of North Carolina at Charlotte, Charlotte, U.S.A NC 28223;Department of Computer Science, University of North Carolina at Charlotte, Charlotte, U.S.A NC 28223;Department of Computer Science, University of North Carolina at Charlotte, Charlotte, U.S.A NC 28223;Department of Computer Science, University of North Carolina at Charlotte, Charlotte, U.S.A NC 28223
Venue:
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Year:
2008

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Abstract

We present a method for detecting common human actions in video, common to athletics and surveillance, using intuitive sketches and motion cues. The framework presented in this paper is an automated end-to-end system which (1) interprets the sketch input, (2) generates a query video based on motion cues, and (3) incorporates a new content-based action descriptor for matching. We apply our method to a publicly-available video repository of many common human actions and show that a video matching the concept of the sketch is generally returned in one of the top three query results.