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.

Lightweight computer vision methods for traffic flow monitoring on low power embedded sensors

Magrini M;Moroni D;Pieri G;Salvetti O
2015

Abstract

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.
2015
Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" - ISTI
Inglese
José Braz, Sebastiano Battiato and Francisco Imai
VISAPP 2015 - 10th International Conference on Computer Vision Theory and Applications
663
670
978-989-758-090-1
https://www.scitepress.org/PublicationsDetail.aspx?ID=2tYms9FzJk4=&t=1
SCITEPRESS - Science and Technology Publications
digital library
STATI UNITI D'AMERICA
Sì, ma tipo non specificato
11-14 March 2015
Berlin, Germany
Real-time Imaging
Embedded Systems
Intelligent Transport Systems (ITS)
I.4.8 IMAGE PROCESSING AND COMPUTER VISION. Scene analysis
I.2.10 ARTIFICIAL INTELLIGENCE. Vision and Scene Understanding
Intelligent Cooperative Sensing for Improved traffic efficiency - Acronimo ICSI - Grant agreement 317671 - Tipo Progetto EU_FP7
4
partially_open
Magrini M.; Moroni D.; Pieri G.; Salvetti O.
273
info:eu-repo/semantics/conferenceObject
04 Contributo in convegno::04.01 Contributo in Atti di convegno
   Intelligent Cooperative Sensing for Improved traffic efficiency
   ICSI
   FP7
   317671
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/293830
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