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/works › Article in conference proceedings › Research › peer-review
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Innovation Through Information Systems - Volume II: A Collection of Latest Research on Technology Issues. ed. / Frederik Ahlemann; Reinhard Schütte; Stefan Stieglitz. Cham: Springer Science and Business Media Deutschland, 2021. p. 642-655 (Lecture Notes in Information Systems and Organisation; Vol. 47).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Parking space management through deep learning – an approach for automated, low-cost and scalable real-time detection of parking space occupancy
AU - Schulte, Michael René
AU - Thiée, Lukas-Walter
AU - Scharfenberger, Jonas
AU - Funk, Burkhardt
N1 - Conference code: 16
PY - 2021/11
Y1 - 2021/11
N2 - 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.
AB - 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.
KW - Deep learning
KW - Design science research
KW - Object detection
KW - Parking space management
KW - Informatics
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=85118170584&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-86797-3_42
DO - 10.1007/978-3-030-86797-3_42
M3 - Article in conference proceedings
AN - SCOPUS:85118170584
SN - 9783030867966
T3 - Lecture Notes in Information Systems and Organisation
SP - 642
EP - 655
BT - Innovation Through Information Systems - Volume II
A2 - Ahlemann, Frederik
A2 - Schütte, Reinhard
A2 - Stieglitz, Stefan
PB - Springer Science and Business Media Deutschland
CY - Cham
T2 - 16th International Conference on Business Information Systems Engineering - WI 2021
Y2 - 9 March 2021 through 11 March 2021
ER -