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We describe a method for using crowd-sourced labor to track motion and ultimately annotate gestures of humans in video. Our chosen platform for deployment, Amazon Mechanical Turk, divides labor into HITs (Human Intelligence Tasks). Given the informational density of video, our task is potentially larger than a traditional HIT that involves processing a block of text or a single image. We exploit redundancies...
This paper explores the idea of extracting three dimensional features from a previously recorded video, in an attempt to provide three dimensional information about a video clip in order to improve the performance of various video analysis tasks. Although video analysis is a very prevalent area of research, the use of 3D features is scarce in the literature due to the inherent difficulties associated...
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