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

Standard

Parking space management through deep learning – an approach for automated, low-cost and scalable real-time detection of parking space occupancy. / Schulte, Michael René; Thiée, Lukas-Walter; Scharfenberger, Jonas; Funk, Burkhardt.

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 GmbH, 2021. p. 642-655 (Lecture Notes in Information Systems and Organisation; Vol. 47).

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

Harvard

Schulte, MR, Thiée, L-W, Scharfenberger, J & Funk, B 2021, Parking space management through deep learning – an approach for automated, low-cost and scalable real-time detection of parking space occupancy. in F Ahlemann, R Schütte & S Stieglitz (eds), Innovation Through Information Systems - Volume II: A Collection of Latest Research on Technology Issues. Lecture Notes in Information Systems and Organisation, vol. 47, Springer Science and Business Media Deutschland GmbH, Cham, pp. 642-655, 16th International Conference on Business Information Systems Engineering - WI 2021, Duisburg, Germany, 09.03.21. https://doi.org/10.1007/978-3-030-86797-3_42

APA

Schulte, M. R., Thiée, L-W., Scharfenberger, J., & Funk, B. (2021). Parking space management through deep learning – an approach for automated, low-cost and scalable real-time detection of parking space occupancy. In F. Ahlemann, R. Schütte, & S. Stieglitz (Eds.), Innovation Through Information Systems - Volume II: A Collection of Latest Research on Technology Issues (pp. 642-655). (Lecture Notes in Information Systems and Organisation; Vol. 47). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-86797-3_42

Vancouver

Schulte MR, Thiée L-W, Scharfenberger J, Funk B. Parking space management through deep learning – an approach for automated, low-cost and scalable real-time detection of parking space occupancy. In Ahlemann F, Schütte R, Stieglitz S, editors, Innovation Through Information Systems - Volume II: A Collection of Latest Research on Technology Issues. Cham: Springer Science and Business Media Deutschland GmbH. 2021. p. 642-655. (Lecture Notes in Information Systems and Organisation). https://doi.org/10.1007/978-3-030-86797-3_42

Bibtex

@inbook{72f14276bfd44238a47c630cafe7aa10,
title = "Parking space management through deep learning – an approach for automated, low-cost and scalable real-time detection of parking space occupancy",
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 = "Deep learning, Design science research, Object detection, Parking space management, Informatics, Business informatics",
author = "Schulte, {Michael Ren{\'e}} and Lukas-Walter Thi{\'e}e and Jonas Scharfenberger and Burkhardt Funk",
year = "2021",
month = nov,
doi = "10.1007/978-3-030-86797-3_42",
language = "English",
isbn = "9783030867966",
series = "Lecture Notes in Information Systems and Organisation",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "642--655",
editor = "Frederik Ahlemann and Reinhard Sch{\"u}tte and Stefan Stieglitz",
booktitle = "Innovation Through Information Systems - Volume II",
address = "Germany",
note = "16th International Conference on Business Information Systems Engineering - WI 2021, WI ; Conference date: 09-03-2021 Through 11-03-2021",
url = "https://wi2021.de/start-2.html",

}

RIS

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 GmbH

CY - Cham

T2 - 16th International Conference on Business Information Systems Engineering - WI 2021

Y2 - 9 March 2021 through 11 March 2021

ER -