Discriminative models for static human-object interactions
We advocate an approach to activity recognition based on modeling contextual
interactions between postured human bodies and nearby objects. We focus on the
difficult task of recognizing actions from static images and formulate the
problem as a latent structured labeling problem. We develop a unified,
discriminative model for such context-based action recognition building on
recent techniques for learning large-scale discriminative models. The resulting
contextual models learned by our system outperform previously published results
on a database of sports actions.
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Text Reference
Chaitanya Desai, Deva Ramanan, and Charless Fowlkes. Discriminative models for static human-object interactions. In IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Structured Prediction. 2010.BibTeX Reference
@INPROCEEDINGS{DesaiRF_CVPR_2010,author = "Desai, Chaitanya and Ramanan, Deva and Fowlkes, Charless",
booktitle = "IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Structured Prediction",
title = "Discriminative models for static human-object interactions",
year = "2010",
tag = "people"
}