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Parking Analytics Framework using Deep Learning

Bilel Benjdira, Anis Koubaa, Wadii Boulila, Adel Ammar

TL;DR

A methodology to monitor car parking areas and to analyze their occupancy in real-time using the Ray Tracing algorithm, based on a combination between image analysis and deep learning techniques to optimize the use of parking areas.

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.

Parking Analytics Framework using Deep Learning

TL;DR

A methodology to monitor car parking areas and to analyze their occupancy in real-time using the Ray Tracing algorithm, based on a combination between image analysis and deep learning techniques to optimize the use of parking areas.

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.
Paper Structure (11 sections, 5 equations, 5 figures, 1 algorithm)

This paper contains 11 sections, 5 equations, 5 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Proposed Methodology
  4. Vehicle detection using YOLO v3
  5. Vehicle tracking using Deep SORT
  6. Manual annotation of the polygon coordinates of the parking slot
  7. Parking occupancy estimation using the Ray tracing algorithm
  8. Experimental Results
  9. Global insights generated using the framework
  10. Deeper insights generated using the framework
  11. Conclusion

Figures (5)

  • Figure 1: Flowchart of the methodology designed for Parking Areas Analysis
  • Figure 2: Parking Area Analysis in real time
  • Figure 3: Number of vehicles existing in the whole parking area during a specific period of time.
  • Figure 4: Heatmap representing the occupancy of every parking slot in seconds during a specific period of time.
  • Figure 5: Heatmap representing the number of cars that occupied every parking slot during a specific period of time.