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Modeling mutual context of object and human pose in human-object interaction activities

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2 Author(s)
Bangpeng Yao ; Comput. Sci. Dept., Stanford Univ., Stanford, CA, USA ; Li Fei-Fei

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, we show that our mutual context model significantly outperforms state-of-the-art in detecting very difficult objects and human poses.

Published in:

Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on

Date of Conference:

13-18 June 2010