Visual Recognition of Multi-Agent Action Using Binary Temporal Relations

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	Similar Articles Articles by Stephen S. Intille Articles by Aaron F. Bobick

1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'99) - Volume 1

Fort Collins, Colorado

June 23-June 25

ISBN: 0-7695-0149-4

	ASCII Text	x
	Stephen S. Intille, Aaron F. Bobick, "Visual Recognition of Multi-Agent Action Using Binary Temporal Relations," Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, vol. 1, pp. 1056, 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'99) - Volume 1, 1999.

	BibTex	x
	@article{ 10.1109/CVPR.1999.786917, author = {Stephen S. Intille and Aaron F. Bobick}, title = {Visual Recognition of Multi-Agent Action Using Binary Temporal Relations}, journal ={Computer Vision and Pattern Recognition, IEEE Computer Society Conference on}, volume = {1}, year = {1999}, issn = {1063-6919}, pages = {1056}, doi = {http://doi.ieeecomputersociety.org/10.1109/CVPR.1999.786917}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Computer Vision and Pattern Recognition, IEEE Computer Society Conference on TI - Visual Recognition of Multi-Agent Action Using Binary Temporal Relations SN - 1063-6919 SP EP A1 - Stephen S. Intille, A1 - Aaron F. Bobick, PY - 1999 KW - multi-agent action recognition KW - motion un-derstanding KW - plan recognition VL - 1 JA - Computer Vision and Pattern Recognition, IEEE Computer Society Conference on ER -

Stephen S. Intille, Massachussetts Institute of Technology

Aaron F. Bobick, Massachussetts Institute of Technology

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CVPR.1999.786917

A probabilistic framework for representing and visually recognizing complex multi-agent action is presented. Motivated by work in model-based object recognition and designed for the recognition of action from visual evidence, the representation has three components: (1) temporal structure descriptions representing the temporal relationships between agent goals, (2) belief networks for probabilistically representing and recognizing individual agent goals from visual evidence, and (3) belief networks automatically generated from the temporal structure descriptions that support the recognition of the complex action. We describe our current work on recognizing American football plays from noisy trajectory data.1

Index Terms:

multi-agent action recognition, motion un-derstanding, plan recognition

Citation:

Stephen S. Intille, Aaron F. Bobick, "Visual Recognition of Multi-Agent Action Using Binary Temporal Relations," cvpr, vol. 1, pp.1056, 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'99) - Volume 1, 1999

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