Grant: Alliance Grants

This project will develop a capability to automatically recognize safety critical events and conditions within a stream of image and video data, acquired from a network of mobile dash cam and stationary street cam sensors, with the aim of improving road safety. The data source will be a large, distributed network of cameras, comprising both mobile dash cams deployed on Geotab-enabled vehicles, and stationary street cams such as are available from municipal infrastructure. Processing of this data will rely on state-of-the-art Machine Learning methods, primarily variations of Deep Convolutional Neural Networks. The outcome of the project will be the development of advanced deep learning based methods to recognize events and characteristics in urban scene data which are related to road safety, such as an unsafe pedestrian crossing or high-volume intersections, or degraded municipal infrastructure such as burned-out street lights or potholes. The project outcome will include a thorough characterization of the effectiveness of these methods, through the design of a set of structured experiments. Further, these methods will be implemented in a standardized development environment, to facilitate transfer to Geotab for subsequent commercialization, and to demonstrate the applicability to the City of Kingston for municipal infrastructure monitoring. The project impact will be to advance the use of machine learning methods to detect safety-critical events and scenarios within images and video of urban scenes. The project has the potential to improve the safety of urban environments, both through the development of advanced driver and/or pedestrian alerts as safety-critical events and conditions are recognized in real-time, as well as through informing the design criteria of roads and associated infrastructure as the root causes of certain events are revealed. Further tangible impact of the project will be novel product offerings from Geotab, as well as identifying opportunities for the City of Kingston to apply these techniques for infrastructure asset tracking.