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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">naturel:rr08-04/IDIAP</subfield>
		</datafield>
		<datafield tag="245" ind1=" " ind2=" ">
			<subfield code="a">Detecting queues at vending machines: a statistical layered approach</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Naturel, Xavier</subfield>
		</datafield>
		<datafield tag="700" ind1=" " ind2=" ">
			<subfield code="a">Odobez, Jean-Marc</subfield>
		</datafield>
		<datafield tag="856" ind1="4" ind2="0">
			<subfield code="i">EXTERNAL</subfield>
			<subfield code="u">http://publications.idiap.ch/attachments/reports/2008/naturel-idiap-rr-08-04.pdf</subfield>
			<subfield code="x">PUBLIC</subfield>
		</datafield>
		<datafield tag="088" ind1=" " ind2=" ">
			<subfield code="a">Idiap-RR-04-2008</subfield>
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		<datafield tag="260" ind1=" " ind2=" ">
			<subfield code="c">2008</subfield>
			<subfield code="b">IDIAP</subfield>
		</datafield>
		<datafield tag="520" ind1=" " ind2=" ">
			<subfield code="a">In this report, a method for monitoring activity at a ticket machine is presented. While this work has been done in the specific context of Turin metro, the proposed model could be applied to other locations and tasks in video-surveillance. Monitoring the activity is based here on event recognition, by modelling directly the events of interest.We especially focus on detecting queues at ticket vending machines. A 2-layer model is proposed. In the first layer, several sub-events are defined and detected using a discriminative approach (SVMs). The second layer uses the result of the first and model the high-level event of interest. Results are assessed on 4 hours of real video footage coming from Turin metro station.</subfield>
		</datafield>
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