Reference number : GC-2017-Q2-00489, amendment number : , agreement type : G, recipient legal name : Bilodeau, Guillaume-Alexandre (École Polytechnique de Montréal), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Collaborative Research and Development Grants - Project, program name french : Subventions de recherche et développement coopérative - projet, program purpose english : The goal of this project is to design new methods to detect and classify road users on streets and parking lots._x000D_
By classification of road users, it is meant to distinguish first bikes from pedestrians and motorized vehicles,_x000D_
and then to distinguish motorized vehicles into subcategories like cars, trucks, buses, ambulances, etc. Finding_x000D_
a solution to this problem will allow our industrial partner Genetec to enhance the capabilities of their products._x000D_
For instance, knowing the category of a vehicle allows verifying if a license plate or an access control device_x000D_
has been stolen or tampered with, or it can be used to restrict access to an area to certain types of road users. To_x000D_
solve this problem, our approach will rely on deep learning techniques to learn to detect and classify road users._x000D_
We will use a hierarchy of Deep Neural Networks (DNNs) to go from a coarse categorization of road users to a_x000D_
finer one to obtain more specific categories. The knowledge gained from the design of this detection and_x000D_
classification method will be transferred to our industrial partner Genetec. This will allow Genetec to remain_x000D_
competitive in the fast evolving video surveillance market. Being able to categorize road users will allow_x000D_
Genetec to develop better tools to monitor traffic and advance the capabilities of their products. As well, both_x000D_
Prs Bilodeau and Saunier are working on applications of computer vision to transportation and in particular for_x000D_
traffic data collection and road safety analysis. Road user classification has not been addressed extensively in_x000D_
the scientific literature. This work will also benefit the computer vision field for finer grain object detection_x000D_
tasks. Finally, this project will train one PhD student in the machine learning and data science fields with skills_x000D_
that are in high demand in the Canadian industry., program purpose french : The goal of this project is to design new methods to detect and classify road users on streets and parking lots._x000D_
By classification of road users, it is meant to distinguish first bikes from pedestrians and motorized vehicles,_x000D_
and then to distinguish motorized vehicles into subcategories like cars, trucks, buses, ambulances, etc. Finding_x000D_
a solution to this problem will allow our industrial partner Genetec to enhance the capabilities of their products._x000D_
For instance, knowing the category of a vehicle allows verifying if a license plate or an access control device_x000D_
has been stolen or tampered with, or it can be used to restrict access to an area to certain types of road users. To_x000D_
solve this problem, our approach will rely on deep learning techniques to learn to detect and classify road users._x000D_
We will use a hierarchy of Deep Neural Networks (DNNs) to go from a coarse categorization of road users to a_x000D_
finer one to obtain more specific categories. The knowledge gained from the design of this detection and_x000D_
classification method will be transferred to our industrial partner Genetec. This will allow Genetec to remain_x000D_
competitive in the fast evolving video surveillance market. Being able to categorize road users will allow_x000D_
Genetec to develop better tools to monitor traffic and advance the capabilities of their products. As well, both_x000D_
Prs Bilodeau and Saunier are working on applications of computer vision to transportation and in particular for_x000D_
traffic data collection and road safety analysis. Road user classification has not been addressed extensively in_x000D_
the scientific literature. This work will also benefit the computer vision field for finer grain object detection_x000D_
tasks. Finally, this project will train one PhD student in the machine learning and data science fields with skills_x000D_
that are in high demand in the Canadian industry., agreement title english : Detection and classification of road users for video surveillance applications, agreement title french : Detection and classification of road users for video surveillance applications, agreement number : CRDPJ, agreement value : 70732.0, agreement start date : 2017-08-23, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2021-2022)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2021-2022)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada