Parking Utilization Assessment Using Deep Learning

Analyzing parking behavior and usage in large open-concept retail centers enables owners and managers to better understand how their parking facility is being used. Most large, open-concept shopping centers are experiencing a parking oversupply problem. Current parking allocation is inefficient and contributes to urban sprawl, large concrete pads that trap solar heat and a waste of valuable real estate resources. Parking studies are generally conducted on foot using a combination of manual tallying or with ground level cameras used to collect imagery of ingress / egress traffic. These techniques are labor intensive and error prone. In this research, we aim to employ artificial intelligence techniques to automate and scale this process. We will use drones to capture aerial imagery of parking facilities, pre-process images, design a human intelligence task for annotation of the acquired images and develop a deep learning-based vehicle detection method to assess parking utilization across multiple observations.

Intern: 
John Taylor Jewell
Faculty Supervisor: 
Yalda Mohsenzadeh;Steven Beauchemin
Province: 
Ontario
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