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Cited by 573

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

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

Yao, Bangpeng, Fei-Fei, Li·2010·2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
DOI 10.1109/cvpr.2010.5540235
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Abstract

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.

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Metadata

Authors
Yao, B., & Fei-Fei, L.
Venue
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Publisher
IEEE
Published
2010-06
Pages
17-24
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2010.5540235
OpenAlex
W2046589395
Cited by
573
References
2
FWCI
41.51
Type
conference paper
Language
en
Open access
closed

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