Our goal is to recognize human actions 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 spatio-temporal 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 spatio-temporal 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.
best-match.avi: Video showing the best
motion descriptor matches for a short running sequence (see Figure 7
player-match.avi: Video showing the best matches drawn from a single player (no smoothing!)
GregWorldCup.avi: Greg in the
World Cup (38 MB!) -- an extended "Do as I Do" action synthesis
and retargetting example.
daid-tennis.avi: "Do as I Do" video for tennis sequence (top: "driver", bottom:"target"). Although the positions of the two figures are different, their actions are matched.