A Sketch-Based Approach for Detecting Common Human Actions
Conference paper
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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.
Keywords
Action Recognition Real Video Joint Location Human Body Model Motion Descriptor
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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