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		<datafield tag="980" ind1=" " ind2=" ">
			<subfield code="a">REPORT</subfield>
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		<datafield tag="970" ind1=" " ind2=" ">
			<subfield code="a">smith:rr06-40/IDIAP</subfield>
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		<datafield tag="245" ind1=" " ind2=" ">
			<subfield code="a">Tracking Attention for Multiple People: Wandering Visual Focus of Attention Estimation</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Smith, Kevin C.</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Ba, Silèye O.</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Odobez, Jean-Marc</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Gatica-Perez, Daniel</subfield>
		</datafield>
		<datafield tag="856" ind1="4" ind2="0">
			<subfield code="i">EXTERNAL</subfield>
			<subfield code="u">http://publications.idiap.ch/attachments/reports/2006/smith-idiap-rr-06-40.pdf</subfield>
			<subfield code="x">PUBLIC</subfield>
		</datafield>
		<datafield tag="088" ind1=" " ind2=" ">
			<subfield code="a">Idiap-RR-40-2006</subfield>
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		<datafield tag="260" ind1=" " ind2=" ">
			<subfield code="c">2006</subfield>
			<subfield code="b">IDIAP</subfield>
		</datafield>
		<datafield tag="500" ind1=" " ind2=" ">
			<subfield code="a">Submitted for publication</subfield>
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		<datafield tag="520" ind1=" " ind2=" ">
			<subfield code="a">The problem of finding the visual focus of attention of multiple people free to move in an unconstrained manner is defined here as the {\em wandering visual focus of attention} (WVFOA) problem. Estimating the WVFOA for multiple unconstrained people is a new and important problem with implications for human behavior understanding and cognitive science, as well as real-world applications. One such application, which we present in this article, monitors the attention passers-by pay to an outdoor advertisement. In our approach to the WVFOA problem, we propose a multi-person tracking solution based on a hybrid Dynamic Bayesian Network that simultaneously infers the number of people in a scene, their body locations, their head locations, and their head pose. It is defined in a joint state-space formulation that allows for the modeling of interactions between people. For inference in the resulting high-dimensional state-space, we propose a trans-dimensional Markov Chain Monte Carlo (MCMC) sampling scheme, which not only handles a varying number of people, but also efficiently searches the state-space by allowing person-part state updates. Our model was rigorously evaluated for tracking quality and ability to recognize people looking at an outdoor advertisement, and the results indicate good performance for these tasks.</subfield>
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