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   <header>
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    <identifier>oai:pumaoai.isti.cnr.it:cnr.isti/cnr.isti/2015-A2-005</identifier>
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    <datestamp>2015-03-23</datestamp>
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      <dc:title>Lightweight computer vision methods for traffic flow monitoring on low power embedded sensors</dc:title>
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      <dc:creator>Magrini, Massimo</dc:creator>
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      <dc:creator>Moroni, Davide</dc:creator>
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      <dc:creator>Pieri, Gabriele</dc:creator>
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      <dc:creator>Salvetti, Ovidio</dc:creator>
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      <dc:subject>Real-time Imaging</dc:subject>
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      <dc:subject>Embedded Systems</dc:subject>
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      <dc:subject>Intelligent Transport Systems (ITS)</dc:subject>
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      <dc:subject>info:eu-repo/classification/acm/I.4.8 IMAGE PROCESSING AND COMPUTER VISION. Scene analysis</dc:subject>
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      <dc:subject>info:eu-repo/classification/acm/I.2.10 ARTIFICIAL INTELLIGENCE. Vision and Scene Understanding</dc:subject>
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      <dc:description>Nowadays pervasive monitoring of traffic flows in urban environment is a topic of great relevance, since the information it is possible to gather may be exploited for a more efficient and sustainable mobility. In this paper, we address the use of smart cameras for assessing the level of service of roads and early detect possible congestion. In particular, we devise a lightweight method that is suitable for use on low power and low cost sensors, resulting in a scalable and sustainable approach to flow monitoring over large areas. We also present the current prototype of an ad hoc device we designed and report experimental results obtained during a field test.</dc:description>
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      <dc:publisher>SCITEPRESS - Science and Technology Publications</dc:publisher>
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      <dc:contributor/>
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      <dc:relation>info:eu-repo/grantAgreement/EC/FP7/317671/EU/Intelligent Cooperative Sensing for Improved traffic efficiency/ICSI</dc:relation>
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      <dc:date>2015</dc:date>
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      <dc:identifier>http://puma.isti.cnr.it/dfdownloadnew.php?ident=cnr.isti/cnr.isti/2015-A2-005</dc:identifier>
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      <dc:language>en</dc:language>
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