Modeling Mutual Context of Object and Human Pose in Human-Object Interaction Activities

author: Fei-Fei Li, Computer Science Department, Stanford University
published: July 19, 2010,   recorded: June 2010,   views: 25192


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Detecting objects in cluttered scenes and estimating articulated human body parts are two challenging problems in computer vision. The difficulty is particularly pronounced in activities involving human-object interactions (e.g. playing tennis), where the relevant object tends to be small or only partially visible, and the human body parts are often self-occluded. We observe, however, that objects and human poses can serve as mutual context to each other – recognizing one facilitates the recognition of the other. In this paper we propose a new random field model to encode the mutual context of objects and human poses in human-object interaction activities. We then cast the model learning task as a structure learning problem, of which the structural connectivity between the object, the overall human pose, and different body parts are estimated through a structure search approach, and the parameters of the model are estimated by a new max-margin algorithm. On a sports data set of six classes of human-object interactions [12], we show that our mutual context model significantly outperforms state-of-theart in detecting very difficult objects and human poses.

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Reviews and comments:

Comment1 jpthank, October 22, 2010 at 9:31 p.m.:

nice presentation

Comment2 karthik, December 21, 2010 at 7:31 p.m.:

Examples are good. Lot of theorems are introduced, if a background material of those are available for download, it will be more helpful. Thank you :]

if there is an update for the video, please post that too.

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