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Grant: Idea to Innovation

University of Ontario Institute of Technology

$20,000.00

Real-time Traffic Analytics from Live Camara Feeds

FY 2021-22

Summary
The proposed system will detect and track vehicles, pedestrians and jaywalkers by using deep learning technique for real-time detection. Surveillance cameras will be installed at specific locations which will stream the real-time traffic data. A deep learning model will be built to detect vehicles and pedestrians and classify road users at high accuracy. The system not only allows users to create lanes and crosswalks to count vehicles and pedestrians but also creates jaywalking boundaries to detect jaywalkers in specific areas. The Deepsort algorithm will be utilized to identify and track the vehicles and pedestrians as well as their direction of movement. The system will allow users to add and manage multi camera streams (i.e., different angles) and features for specific cameras such as vehicle counting, pedestrian counting or jaywalking.The system illustrates various easy-to-understand dashboard visualizations to represent the multiple captured vehicles, pedestrians and jaywalkers in real-time. Furthermore, a warning system will be developed to alerts designated persons through SMS and/or email when an event of interest occurs. These insights can the system users to make informed decision for better traffic management safer roads.
Program
Idea to Innovation
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
571255-2022
Name nom
Elgazzar, Khalid
Department département
Electrical, Computer and Software Engineering
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
2021
Competition year année de concours
2022
Award amount
20000
Program id
I2IPJ
Program name en
Idea to Innovation
Program name fr
De l'idée à l'innovation
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
801
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Computer software
Area of application fr
Logiciel
Research subject code
2800
Research subject group en
Artificial intelligence
Research subject group fr
Intelligence artificielle
Research subject en
Artificial intelligence (computer vision, use2603)
Research subject fr
Intelligence artificielle (vision artificielle, utiliser 2603)
Installment
inst:1
Partie
1
Nb partie
1
Application title
Real-time Traffic Analytics from Live Camara Feeds
Keyword
NSERC
Application summary
The proposed system will detect and track vehicles, pedestrians and jaywalkers by using deep learning technique for real-time detection. Surveillance cameras will be installed at specific locations which will stream the real-time traffic data. A deep learning model will be built to detect vehicles and pedestrians and classify road users at high accuracy. The system not only allows users to create lanes and crosswalks to count vehicles and pedestrians but also creates jaywalking boundaries to detect jaywalkers in specific areas. The Deepsort algorithm will be utilized to identify and track the vehicles and pedestrians as well as their direction of movement. The system will allow users to add and manage multi camera streams (i.e., different angles) and features for specific cameras such as vehicle counting, pedestrian counting or jaywalking.The system illustrates various easy-to-understand dashboard visualizations to represent the multiple captured vehicles, pedestrians and jaywalkers in real-time. Furthermore, a warning system will be developed to alerts designated persons through SMS and/or email when an event of interest occurs. These insights can the system users to make informed decision for better traffic management safer roads.