Aaron Bobick: Representation and Recognition of Human Behavior for Perception
We have developed several approaches to the representation and
recognition of human behavior, focusing primarily computer vision. We
divide behaviors into movement, activity, and action. Movements are
the most atomic primitives, requiring no contextual or sophisticated
sequence knowledge to be recognized; movement is often represented and
recognized using either view-invariant or view specific geometric
techniques. Activity refers to sequences of movements, represented
primarily by statistical descriptions; much of the recent work in
gesture understanding falls within this category of behavior
recognition. Finally, actions are larger scale events that typically
include interaction with the environment and causal relationships;
action understanding straddles the division between perception and
cognition, computer vision and artificial intelligence/cognitive
science. Fundamental questions underlying these techniques include
how is time represented, what is the relationship between structural
and statistical representations of behavior, and can the recognition
of high level actions - involving, for example, intentionality - be
achieved by compiled visual routines. I will present examples of our
work in each of these areas covering domains ranging from the
recognition of aerobics movements, to recovering parametric gestures,
to visual surveillance, to interpreting football plays. I will also
show video of some interactive spaces that leverage the techniques
described.