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. Findinga 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 devicehas been stolen or tampered with, or it can be used to restrict access to an area to certain types of road users. Tosolve 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 afiner one to obtain more specific categories. The knowledge gained from the design of this detection andclassification method will be transferred to our industrial partner Genetec. This will allow Genetec to remaincompetitive in the fast evolving video surveillance market. Being able to categorize road users will allowGenetec to develop better tools to monitor traffic and advance the capabilities of their products. As well, bothPrs Bilodeau and Saunier are working on applications of computer vision to transportation and in particular fortraffic data collection and road safety analysis. Road user classification has not been addressed extensively inthe scientific literature. This work will also benefit the computer vision field for finer grain object detectiontasks. Finally, this project will train one PhD student in the machine learning and data science fields with skillsthat are in high demand in the Canadian industry.