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

Michael René Schulte (), Lukas-Walter Thiée (), Jonas Scharfenberger () and Burkhardt Funk ()
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Michael René Schulte: Leuphana University
Lukas-Walter Thiée: Leuphana University
Jonas Scharfenberger: Leuphana University
Burkhardt Funk: Leuphana University

A chapter in Innovation Through Information Systems, 2021, pp 642-655 from Springer

Abstract: 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.

Keywords: Design science research; Parking space management; Object detection; Deep learning (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-030-86797-3_42

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DOI: 10.1007/978-3-030-86797-3_42

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