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Parking Space Management Through Deep Learning – An Approach for Automated, Low-Cost and Scalable Real-Time Detection of Parking Space Occupancy

In: Innovation Through Information Systems

Author

Listed:
  • Michael René Schulte

    (Leuphana University)

  • Lukas-Walter Thiée

    (Leuphana University)

  • Jonas Scharfenberger

    (Leuphana University)

  • Burkhardt Funk

    (Leuphana University)

Abstract

Balancing parking space capacities and distributing capacity information play an important role in modern metropolitan life and urban land use management. They promise not only optimal urban land use and reductions of search time for suitable parking, but also contribute to a lower fuel consumption. Based on a design science research approach we develop a solution to parking space management through deep learning and aspire to design a camera-based, low-cost, scalable, real-time detection of occupied parking spaces. We evaluate the solution by building a prototype to track cars on parking lots that improves prior work by using a TensorFlow deep neural network with YOLOv4 and DeepSORT. Additionally, we design a web interface to visualize parking capacity and provide further information, such as average parking times. This work contributes to camera-based parking space management on public, open-air parking lots.

Suggested Citation

  • Michael René Schulte & Lukas-Walter Thiée & Jonas Scharfenberger & Burkhardt Funk, 2021. "Parking Space Management Through Deep Learning – An Approach for Automated, Low-Cost and Scalable Real-Time Detection of Parking Space Occupancy," Lecture Notes in Information Systems and Organization, in: Frederik Ahlemann & Reinhard Schütte & Stefan Stieglitz (ed.), Innovation Through Information Systems, pages 642-655, Springer.
    • Handle: RePEc:spr:lnichp:978-3-030-86797-3_42
    DOI: 10.1007/978-3-030-86797-3_42
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