Tracking Vehicle Cruising in an Open Parking Lot Using Deep Learning and Kalman Filter
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 a...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/1812647 |
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| _version_ | 1832565253382078464 |
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| author | Vijay Paidi Hasan Fleyeh Johan Håkansson Roger G. Nyberg |
| author_facet | Vijay Paidi Hasan Fleyeh Johan Håkansson Roger G. Nyberg |
| author_sort | Vijay Paidi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ae4dfe95396d431b847f30ef41aff56d |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-ae4dfe95396d431b847f30ef41aff56d2025-02-03T01:08:52ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/18126471812647Tracking Vehicle Cruising in an Open Parking Lot Using Deep Learning and Kalman FilterVijay Paidi0Hasan Fleyeh1Johan Håkansson2Roger G. Nyberg3Dalarna University, Faculty of Data & Information Sciences, Borlänge, SwedenDalarna University, Faculty of Data & Information Sciences, Borlänge, SwedenDalarna University, Faculty of Data & Information Sciences, Borlänge, SwedenDalarna University, Faculty of Data & Information Sciences, Borlänge, SwedenDue 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.http://dx.doi.org/10.1155/2021/1812647 |
| spellingShingle | Vijay Paidi Hasan Fleyeh Johan Håkansson Roger G. Nyberg Tracking Vehicle Cruising in an Open Parking Lot Using Deep Learning and Kalman Filter Journal of Advanced Transportation |
| title | Tracking Vehicle Cruising in an Open Parking Lot Using Deep Learning and Kalman Filter |
| title_full | Tracking Vehicle Cruising in an Open Parking Lot Using Deep Learning and Kalman Filter |
| title_fullStr | Tracking Vehicle Cruising in an Open Parking Lot Using Deep Learning and Kalman Filter |
| title_full_unstemmed | Tracking Vehicle Cruising in an Open Parking Lot Using Deep Learning and Kalman Filter |
| title_short | Tracking Vehicle Cruising in an Open Parking Lot Using Deep Learning and Kalman Filter |
| title_sort | tracking vehicle cruising in an open parking lot using deep learning and kalman filter |
| url | http://dx.doi.org/10.1155/2021/1812647 |
| work_keys_str_mv | AT vijaypaidi trackingvehiclecruisinginanopenparkinglotusingdeeplearningandkalmanfilter AT hasanfleyeh trackingvehiclecruisinginanopenparkinglotusingdeeplearningandkalmanfilter AT johanhakansson trackingvehiclecruisinginanopenparkinglotusingdeeplearningandkalmanfilter AT rogergnyberg trackingvehiclecruisinginanopenparkinglotusingdeeplearningandkalmanfilter |