Detection and classification of road users for video surveillance applications

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The goal of this project is to design new methods to detect and classify road users on streets and parking lots.**By classification of road users, it is meant to distinguish first bikes from pedestrians and motorized vehicles,**and then to distinguish motorized vehicles into subcategories like cars, trucks, buses, ambulances, etc. Finding**a solution to this problem will allow our industrial partner Genetec to enhance the capabilities of their products.**For instance, knowing the category of a vehicle allows verifying if a license plate or an access control device**has been stolen or tampered with, or it can be used to restrict access to an area to certain types of road users. To**solve this problem, our approach will rely on deep learning techniques to learn to detect and classify road users.**We will use a hierarchy of Deep Neural Networks (DNNs) to go from a coarse categorization of road users to a**finer one to obtain more specific categories. The knowledge gained from the design of this detection and**classification method will be transferred to our industrial partner Genetec. This will allow Genetec to remain**competitive in the fast evolving video surveillance market. Being able to categorize road users will allow**Genetec to develop better tools to monitor traffic and advance the capabilities of their products. As well, both**Prs Bilodeau and Saunier are working on applications of computer vision to transportation and in particular for**traffic data collection and road safety analysis. Road user classification has not been addressed extensively in**the scientific literature. This work will also benefit the computer vision field for finer grain object detection**tasks. Finally, this project will train one PhD student in the machine learning and data science fields with skills**that are in high demand in the Canadian industry.