Deep Learning Based Parking Vacancy Detection for Smart Cities

Xu, Qiangwen
Sun, Mengyao
Fu, Bailing
Zhao, Yijun
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Parking shortage is a major problem in modern cities. Drivers cruising in search of a parking space directly translate into frustration, traffic congestion, and excessive carbon emission. We introduce a simple and effective deep learning-based parking space notification (PSN) system to inform drivers of new parking availabilities and re-occupancy of the freed spaces. Our system is particularly designed to target areas with severe parking shortages (i.e., nearly all parking spaces are occupied), a situation that allows us to convert the problem of detecting parking vacancies into recognizing vehicles leaving from their stationary positions. Our PSN system capitalizes on a calibrated Mask R-CNN model and a unique adaptation of the IoU concept to track the changes of vehicle positions in a video stream. We evaluated PSN using videos from a CCTV camera installed at a private parking lot and publicly available YouTube videos. The PSN system successfully captured all new parking vacancies arising from leaving vehicles with no false positive detections. Prompt notification messages were sent to users via cloud messaging services.
Smart (City) and Data Streaming Application Development: Challenges and Experiences, deep learning, iot, object localization, parking vacancy detection, smart cities
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