(Enter summary)
Abstract: In this paper, we present techniques for characterizing complex,
multi-tasked activities that require both exemplars and
models. Exemplars are used to represent object context,
image features, and motion appearances to label domainspecific
events. Then, by representing each event with a
unique symbol, a sequence of interactions can be described
as an ordered symbolic string. A model of stochastic contextfree
grammar, which is developed using underlying rules of
an activity, provides the... (Update)
Context of citations to this paper: More
.... e.g. natural language, XML, DNA sequences 00] handwriting [FST98] plan recognition [BVW00] visual action recogntion [IB00, ME01, Hoe01], and spatial navigation [TRM01, BVW01] HHMMs are less expressive than stochastic context free grammars (SCFGs) since they only...
.... and complex activities are represented as a hierarchical combination of simpler activities conditioned on various temporal constraints[15, 11, 16]. To a large extend, research in this direction has been bogged down by two fundamental problems. First, we are limited in our...
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BibTeX entry: (Update)
D. Moore and I. Essa. Recognizing multitasked activities using stochastic context-free grammar. In CVPR Workshop on Models vs Exemplars in Computer Vision, 2001. http://citeseer.ist.psu.edu/moore01recognizing.html More
@misc{ moore01recognizing,
author = "D. Moore and I. Essa",
title = "Recognizing multitasked activities using stochastic context-free grammar",
text = "D. Moore and I. Essa. Recognizing multitasked activities using stochastic
context-free grammar. In CVPR Workshop on Models vs Exemplars in Computer
Vision, 2001.",
year = "2001",
url = "citeseer.ist.psu.edu/moore01recognizing.html" }
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