Recognizing proxemics in personal photos
Proxemics is the study of how people interact. We
present a computational formulation of visual proxemics by
attempting to label each pair of people in an image with a
subset of physically-based “touch codes.” A baseline
approach would be to first perform pose estimation and then
detect the touch codes based on the estimated joint locations.
We found that this sequential approach does not perform
well because pose estimation step is too unreliable for
images of interacting people, due to difficulties with
occlusion and limb ambiguities. Instead, we propose a direct
approach where we build an articulated model tuned for
each touch code. Each such model contains two people,
connected in an appropriate manner for the touch code in
question. We fit this model to the image and then base
classification on the fitting error. Experiments show that this
approach significantly outperforms the sequential baseline
as well as other related approaches.
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Text Reference
Yi Yang, Simon Baker, Anitha Kannan, and Deva Ramanan. Recognizing proxemics in personal photos. In CVPR, 3522–3529. 2012.BibTeX Reference
@inproceedings{YangBKR_CVPR_2012,author = "Yang, Yi and Baker, Simon and Kannan, Anitha and Ramanan, Deva",
title = "Recognizing proxemics in personal photos",
booktitle = "CVPR",
year = "2012",
pages = "3522-3529"
}