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• W3141200356 abstract "Our goal is to recognize human action at a distance, at resolutions where a whole person may be, say, 30 pixels tall. We introduce a novel motion descriptor based on optical flow measurements in a spatiotemporal volume for each stabilized human figure, and an associated similarity measure to be used in a nearest-neighbor framework. Making use of noisy optical flow measurements is the key challenge, which is addressed by treating optical flow not as precise pixel displacements, but rather as a spatial pattern of noisy measurements which are carefully smoothed and aggregated to form our spatiotemporal motion descriptor. To classify the action being performed by a human figure in a query sequence, we retrieve nearest neighbor(s) from a database of stored, annotated video sequences. We can also use these retrieved exemplars to transfer 2D/3D skeletons onto the figures in the query sequence, as well as two forms of data-based action synthesis do as I do and do as I say. Results are demonstrated on ballet, tennis as well as football datasets." @default.
• W3141200356 created "2021-04-13" @default.
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• W3141200356 date "2003-01-01" @default.
• W3141200356 modified "2023-10-11" @default.
• W3141200356 title "Recognizing action at a distance" @default.
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• W3141200356 doi "https://doi.org/10.1109/iccv.2003.1238420" @default.
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