@article{ref1,
title="Explaining activities as consistent groups of events",
journal="International journal of computer vision",
year="2012",
author="Damen, Dima and Hogg, David",
volume="98",
number="1",
pages="83-102",
abstract="We propose a method for disambiguating uncertain detections of events by seeking global explanations for activities. Given a noisy visual input, and exploiting our knowledge of the activity and its constraints, one can provide a consistent set of events explaining all the detections. The paper presents a complete framework that starts with a general way to formalise the set of global explanations for a given activity using attribute multiset grammars (AMG). An AMG combines the event hierarchy with the necessary features for recognition and algebraic constraints defining allowable combinations of events and features. Parsing a set of detections by such a grammar finds a consistent set of events that satisfies the activity's constraints. Each parse tree has a posterior probability in a Bayesian sense. To find the best parse tree, the grammar and a finite set of detections are mapped into a Bayesian network. The set of possible labellings of the Bayesian network corresponds to the set of all parse trees for a given set of detections. We compare greedy, multiple-hypotheses trees, reversible jump MCMC, and integer programming for finding the Maximum a Posteriori (MAP) solution over the space of explanations. The framework is tested for two applications; the activity in a bicycle rack and around a building entrance.<p /> <p>Language: en</p>",
language="en",
issn="0920-5691",
doi="10.1007/s11263-011-0497-0",
url="http://dx.doi.org/10.1007/s11263-011-0497-0"
}