Parking Analytics Framework using Deep Learning
This addresses parking inefficiencies for city drivers and managers, but it is incremental as it applies existing techniques to a specific domain.
The paper tackles real-time parking occupancy analysis by developing a deep learning-based framework that combines vehicle detection, tracking, manual annotation, and ray tracing for occupancy estimation, aiming to optimize parking usage and reduce driver search time.
With the number of vehicles continuously increasing, parking monitoring and analysis are becoming a substantial feature of modern cities. In this study, we present a methodology to monitor car parking areas and to analyze their occupancy in real-time. The solution is based on a combination between image analysis and deep learning techniques. It incorporates four building blocks put inside a pipeline: vehicle detection, vehicle tracking, manual annotation of parking slots, and occupancy estimation using the Ray Tracing algorithm. The aim of this methodology is to optimize the use of parking areas and to reduce the time wasted by daily drivers to find the right parking slot for their cars. Also, it helps to better manage the space of the parking areas and to discover misuse cases. A demonstration of the provided solution is shown in the following video link: https://www.youtube.com/watch?v=KbAt8zT14Tc.