Parking space management through deep learning – an approach for automated, low-cost and scalable real-time detection of parking space occupancy

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review


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.

Original languageEnglish
Title of host publicationInnovation Through Information Systems - Volume II : A Collection of Latest Research on Technology Issues
EditorsFrederik Ahlemann, Reinhard Schütte, Stefan Stieglitz
Number of pages14
Place of PublicationCham
PublisherSpringer Science and Business Media Deutschland GmbH
Publication date11.2021
ISBN (Print)9783030867966
ISBN (Electronic)978-3-030-86797-3
Publication statusPublished - 11.2021
Event16th International Conference on Business Information Systems Engineering - WI 2021 - Universität Duisburg - Essen, Duisburg, Germany
Duration: 09.03.202111.03.2021
Conference number: 16