Behind the Scenes: Decoding Intent from First Person Video

Park, Hyun Soo

Title:

Behind the Scenes: Decoding Intent from First Person Video

dc.contributor.author	Park, Hyun Soo
dc.contributor.corporatename	Georgia Institute of Technology. Institute for Robotics and Intelligent Machines	en_US
dc.contributor.corporatename	University of Minnesota. Department of Computer Science and Engineering	en_US
dc.date.accessioned	2017-02-08T12:02:34Z
dc.date.available	2017-02-08T12:02:34Z
dc.date.issued	2017-02-01
dc.description	Presented on [date] from 12:00 pm - 1:00 pm in the [venue]	en_US
dc.description	Presented on February 1, 2017 from 12:00 p.m.-1:00 p.m. in the Marcus Nanotechnology Building, Rooms 1116-1118 on the Georgia Tech campus.	en_US
dc.description	Hyun Soo Park is an assistant professor in the Department of Computer Science and Engineering at the University of Minnesota. He is interested in understanding human visual sensorimotor behaviors from first-person cameras. Prior to joining the UMN, he was a postdoctoral fellow in the GRASP Lab at the University of Pennsylvanla. Park earned his Ph.D. from Camegle Mallon University.	en_US
dc.description	Runtime: 56:30 minutes	en_US
dc.description.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.	en_US
dc.format.extent	56:30 minutes
dc.identifier.uri	http://hdl.handle.net/1853/56436
dc.language.iso	en_US	en_US
dc.publisher	Georgia Institute of Technology	en_US
dc.relation.ispartofseries	IRIM Seminar Series
dc.subject	Computer vision	en_US
dc.subject	Wearable camera	en_US
dc.title	Behind the Scenes: Decoding Intent from First Person Video	en_US
dc.type	Moving Image
dc.type.genre	Lecture
dspace.entity.type	Publication
local.contributor.corporatename	Institute for Robotics and Intelligent Machines (IRIM)
local.relation.ispartofseries	IRIM Seminar Series
relation.isOrgUnitOfPublication	66259949-abfd-45c2-9dcc-5a6f2c013bcf
relation.isSeriesOfPublication	9bcc24f0-cb07-4df8-9acb-94b7b80c1e46