Grant: Engage Grants Program
University of Ontario Institute of Technology
$24,978.00
Camera-based vehicle detection and classification
FY 2014-15
Summary
Traffic congestion in big urban centers cost billions of dollars yearly to commuters. It also adversely affects the health and quality-of-life of commuters. Given current population trends, it seems that traffic congestion will only worsen over time unless drastic measures are taken to curb traffic growth. One low-cost strategy that cities and municipalities are exploring for dealing with burgeoning traffic is the use of intelligent traffic phasing systems. These systems route traffic through road networks while minimizing travel times, reducing stops and saving fuel costs. Standard Power Corporation is a Canadian supplier/consultant/OEM of intelligent traffic detection hardware and systems. They provide road traffic data gathering equipment to a number of municipalities across Canada. In this project, Dr. Qureshi's Visual Computing Lab at the University of Ontario Institute of Technology will work with Standard Power Corporation to develop a camera-based vehicle detection and classification system. The system will use traffic cameras mounted on road intersections to classify vehicles present at the scene. The developed system will provide a rich source for data for subsequent traffic monitoring and analysis, including improved ways to control traffic intersections to ease traffic congestion.
Program
Engage Grants Program
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
472204-2014
Name nom
Qureshi, Faisal
Department département
Science, Faculty of
Organization id
19222
Institution établissement
University of Ontario Institute of Technology
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2014
Competition year année de concours
2014
Award amount
24978
Program id
EGP
Program name en
Engage Grants Program
Program name fr
Programme de subventions d'engagement partenariat
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
1553
Committee name en
Ontario Internal Decision Committee
Committee name fr
Comité de décision interne pour l'Ontario
Area of application code
802
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Information systems and technology
Area of application fr
Systèmes d'information et technologie de l'information
Research subject code
2708
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Image processing
Research subject fr
Traitement des images
Partie
1
Nb partie
1
Application title
Camera-based vehicle detection and classification
Keyword
NSERC
Application summary
Traffic congestion in big urban centers cost billions of dollars yearly to commuters. It also adversely affects the health and quality-of-life of commuters. Given current population trends, it seems that traffic congestion will only worsen over time unless drastic measures are taken to curb traffic growth. One low-cost strategy that cities and municipalities are exploring for dealing with burgeoning traffic is the use of intelligent traffic phasing systems. These systems route traffic through road networks while minimizing travel times, reducing stops and saving fuel costs. Standard Power Corporation is a Canadian supplier/consultant/OEM of intelligent traffic detection hardware and systems. They provide road traffic data gathering equipment to a number of municipalities across Canada. In this project, Dr. Qureshi's Visual Computing Lab at the University of Ontario Institute of Technology will work with Standard Power Corporation to develop a camera-based vehicle detection and classification system. The system will use traffic cameras mounted on road intersections to classify vehicles present at the scene. The developed system will provide a rich source for data for subsequent traffic monitoring and analysis, including improved ways to control traffic intersections to ease traffic congestion.