Title:
Behind the Scenes: Decoding Intent from First Person Video
Behind the Scenes: Decoding Intent from First Person Video
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Authors
Park, Hyun Soo
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Abstract
A first person video records not only what is out in the environment but also what is in our head
(intention and attention) at the time via social and physical interactions. It is invisible but it can be
revealed by fixation, camera motion, and visual semantics. In this talk, I will present a computational
model to decode our intention and attention from first person cameras when interacting with (1) scene
and (2) people. A person exerts his/her intention through applying physical force and torque to scenes and objects,
which effects in visual sensation. We leverage the first person visual sensation to precisely compute
force and torque that the first person experienced by integrating visual semantics, 3D reconstruction,
and inverse optimal control. Such visual sensation also allows associating with our past experiences
that eventually provide a strong cue to predict future activities. When interacting with other people,
social attention is a medium that controls group behaviors, e.g., how they form a group and move. We
learn the geometric and visual relationship between group behaviors and social attention measured from first person cameras. Based on the learned relationship, we derive a predictive model to localize
social attention from third person view.
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Date Issued
2017-02-01
Extent
56:30 minutes
Resource Type
Moving Image
Resource Subtype
Lecture