"Automatic Annotation of Everyday Movements"
describes a system that can annotate a video sequence with:
a description of the appearance of each actor; when the actor is in
view; and a representation of the actor's activity while in view. The
system does not require a fixed background, and is automatic. The
system works by (1) tracking people in 2D and then, using an annotated
motion capture dataset, (2) synthesizing an annotated 3D motion
sequence matching the 2D tracks. The 3D motion capture data is manually
annotated off-line using a class structure that describes everyday
motions and allows motion annotations to be composed --- one may jump
while running, for example. Descriptions computed from video of real
motions show that the method is accurate.
Ramanan, D., Forsyth, D. A. "Automatic Annotation of
Everyday Movements." Neural Info. Proc. Systems (NIPS),
Vancouver, Canada, Dec 2003.
Journal version (draft)
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