Parking Analytics Framework using Deep Learning

TL;DR

This paper introduces a deep learning-based framework for real-time parking occupancy analysis, combining vehicle detection, tracking, manual annotation, and ray tracing to optimize parking space utilization.

Contribution

It presents a novel integrated pipeline for parking monitoring that combines image analysis, deep learning, and ray tracing for occupancy estimation.

Findings

Effective real-time parking occupancy detection

Improved parking space management

Potential reduction in drivers' search time

Abstract

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.