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Tracking Vehicle Cruising in an Open Parking Lot Using Deep Learning and Kalman Filter
Dalarna University, School of Information and Engineering, Microdata Analysis. (Smart Parking)ORCID iD: 0000-0002-2078-3327
Dalarna University, School of Information and Engineering, Computer Engineering.ORCID iD: 0000-0002-1429-2345
Dalarna University, School of Information and Engineering, Microdata Analysis.ORCID iD: 0000-0003-4871-833X
Dalarna University, School of Information and Engineering, Informatics.ORCID iD: 0000-0003-4812-4988
2021 (English)In: Journal of Advanced Transportation, ISSN 0197-6729, E-ISSN 2042-3195, article id 1812647Article in journal (Refereed) Published
Abstract [en]

Due to the lack of wide availability of parking assisting applications, vehicles tend to cruise more than necessary to find an empty parking space. This problem is evident globally and the intensity of the problem varies based on the demand of parking spaces. It is a well-known hypothesis that the amount of cruising by a vehicle is dependent on the availability of parking spaces. However, the amount of cruising that takes place in search of parking spaces within a parking lot is not researched. This lack of research can be due to privacy and illumination concerns with suitable sensors like visual cameras. The use of thermal cameras offers an alternative to avoid privacy and illumination problems. Therefore, this paper aims to develop and demonstrate a methodology to detect and track the cruising patterns of multiple moving vehicles in an open parking lot. The vehicle is detected using Yolov3, modified Yolo, and custom Yolo deep learning architectures. The detected vehicles are tracked using Kalman filter and the trajectory of multiple vehicles is calculated on an image. The accuracy of modified Yolo achieved a positive detection rate of 91% while custom Yolo and Yolov3 achieved 83% and 75%, respectively. The performance of Kalman filter is dependent on the efficiency of the detector and the utilized Kalman filter facilitates maintaining data association during moving, stationary, and missed detection. Therefore, the use of deep learning algorithms and Kalman filter facilitates detecting and tracking multiple vehicles in an open parking lot.

Place, publisher, year, edition, pages
Hindawi Publishing Corporation, 2021. article id 1812647
National Category
Information Systems
Identifiers
URN: urn:nbn:se:du-37969DOI: 10.1155/2021/1812647ISI: 000692912400003Scopus ID: 2-s2.0-85114610711OAI: oai:DiVA.org:du-37969DiVA, id: diva2:1587607
Available from: 2021-08-25 Created: 2021-08-25 Last updated: 2023-04-14Bibliographically approved
In thesis
1. Parking support model for open parking lots
Open this publication in new window or tab >>Parking support model for open parking lots
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Parking is a common process performed by vehicle drivers when they arrive at their destination. It is considered to be the last mile transportation problem of personal vehicles. Some of the common problems observed by drivers are additional cruising, congestion, pollution, and driver frustration. This thesis is focused on open parking lots that provide free parking spaces. Since parking spaces are provided free, open parking lots are in high demand leading to additional cruising and pollution. One of the primary reasons for these problems is the lack of information on parking availability. Such information can be provided using a parking support model, or a smart parking system. As open parking lots do not provide any direct return on investments, no parking support models were available on the market. Therefore, this thesis aims to develop a parking support model suitable for open parking lots which can facilitate in providing real-time and short-term forecast of parking availability. This thesis also examines the magnitude of additional cruising and CO2 emissions observed in an open parking lot. A thermal camera was utilized for collecting data on open parking lots as it is not susceptible to varying illumination and environmental conditions. Since there were no pre-trained algorithms for enabling object detection using thermal camera images, a dataset was created with varying environmental and illumination conditions. This dataset was utilized by deep learning algorithms to facilitate multi-object, real-time detection. The developed parking support model facilitates in providing a real-time and short-term forecast of parking availability. Despite the use of low volume of data, the methods utilized in this thesis facilitated providing better detection and forecasting results. Algorithms, such as ResNet18 and Yolo, facilitated in providing real-time, multi-object detection with high accuracy. Similarly, a short-term forecast of parking availability was provided for the open parking lot using methods such as the Ensemble-based method, LSTM and SARIMAX. Ensemble-based method and LSTM provided better test prediction results with lower errors compared to SARIMAX. A new CO2 emissions model was proposed to estimate the magnitude of emissions observed at an open parking lot. The mean CO2 emissions of additional cruising is 2.7 times more than optimal cruising. Despite the individual CO2 emissions of vehicles being lower, aggregating CO2 emissions from multiple vehicles leads to higher pollution. This problem can be reduced by utilizing the parking support model.

Place, publisher, year, edition, pages
Borlänge: Högskolan Dalarna, 2022
Series
Dalarna Doctoral Dissertations ; 21
Keywords
parking, deep learning, pollution, cruising, detection, tracking, forecasting
National Category
Transport Systems and Logistics Computer and Information Sciences
Identifiers
urn:nbn:se:du-41094 (URN)978-91-88679-36-9 (ISBN)
Public defence
2022-06-03, room 311, 10:30 (English)
Opponent
Supervisors
Available from: 2022-05-03 Created: 2022-03-24 Last updated: 2023-08-17Bibliographically approved

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Paidi, VijayFleyeh, HasanHåkansson, JohanNyberg, Roger G.

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