Paper in ICCV 1999 on “Exploiting Human Actions and Object Context for Recognition Tasks”

Citation

D. Moore, I. Essa, M. Hayes: Exploiting Human Actions and Object Context for Recognition Tasks. In: IEEE International Conference on Computer Vision (ICCV), pp. 80–86, IEEE Computer Society Corfu, Greece, 1999.

Abstract

Our goal is to exploit human motion and object context to perform action recognition and object classification. Towards this end, we introduce a framework for recognizing actions and objects by measuring image-, object- and action-based information from video. Hidden Markov models are combined with object context to classify hand actions, which are aggregated by a Bayesian classifier to summarize activities. We also use Bayesian methods to differentiate the class of unknown objects by evaluating detected actions along with low-level, extracted object features. Our approach is appropriate for locating and classifying objects under a variety of conditions including full occlusion. We show experiments where both familiar and previously unseen objects are recognized using action and context information.

Links

BibTeX (Download)

@inproceedings{1999-Moore-EHAOCRT,
title = {Exploiting Human Actions and Object Context for Recognition Tasks},
author = {D. Moore and I. Essa and M. Hayes},
url = {https://ieeexplore.ieee.org/document/791201
},
doi = {10.1109/ICCV.1999.791201},
year  = {1999},
date = {1999-01-01},
urldate = {1999-01-01},
booktitle = {IEEE International Conference on Computer Vision (ICCV)},
pages = {80--86},
address = {Corfu, Greece},
organization = {IEEE Computer Society},
abstract = {Our goal is to exploit human motion and object context to perform action recognition and object classification. Towards this end, we introduce a framework for recognizing actions and objects by measuring image-, object- and action-based information from video. Hidden Markov models are combined with object context to classify hand actions, which are aggregated by a Bayesian classifier to summarize activities. We also use Bayesian methods to differentiate the class of unknown objects by evaluating detected actions along with low-level, extracted object features. Our approach is appropriate for locating and classifying objects under a variety of conditions including full occlusion. We show experiments where both familiar and previously unseen objects are recognized using action and context information.
},
keywords = {activity recognition, computer vision, ICCV},
pubstate = {published},
tppubtype = {inproceedings}
}