Real-time monitoring of traffic parameters
Abstract This study deals with the problem of rea-time obtaining quality data on the road traffic parameters based on the static street video surveillance camera data. The existing road traffic monitoring solutions are based on the use of traffic cameras located directly above the carriageways, whic...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2020-10-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40537-020-00358-x |
| _version_ | 1819006919089586176 |
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| author | Kirill Khazukov Vladimir Shepelev Tatiana Karpeta Salavat Shabiev Ivan Slobodin Irakli Charbadze Irina Alferova |
| author_facet | Kirill Khazukov Vladimir Shepelev Tatiana Karpeta Salavat Shabiev Ivan Slobodin Irakli Charbadze Irina Alferova |
| author_sort | Kirill Khazukov |
| collection | DOAJ |
| description | Abstract This study deals with the problem of rea-time obtaining quality data on the road traffic parameters based on the static street video surveillance camera data. The existing road traffic monitoring solutions are based on the use of traffic cameras located directly above the carriageways, which allows one to obtain fragmentary data on the speed and movement pattern of vehicles. The purpose of the study is to develop a system of high-quality and complete collection of real-time data, such as traffic flow intensity, driving directions, and average vehicle speed. At the same time, the data is collected within the entire functional area of intersections and adjacent road sections, which fall within the street video surveillance camera angle. Our solution is based on the use of the YOLOv3 neural network architecture and SORT open-source tracker. To train the neural network, we marked 6000 images and performed augmentation, which allowed us to form a dataset of 4.3 million vehicles. The basic performance of YOLO was improved using an additional mask branch and optimizing the shape of anchors. To determine the vehicle speed, we used a method of perspective transformation of coordinates from the original image to geographical coordinates. Testing of the system at night and in the daytime at six intersections showed the absolute percentage accuracy of vehicle counting, of no less than 92%. The error in determining the vehicle speed by the projection method, taking into account the camera calibration, did not exceed 1.5 km/h. |
| first_indexed | 2024-12-21T00:16:20Z |
| format | Article |
| id | doaj.art-0666307e04e446029bb8bc7267091848 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-12-21T00:16:20Z |
| publishDate | 2020-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-0666307e04e446029bb8bc72670918482022-12-21T19:22:12ZengSpringerOpenJournal of Big Data2196-11152020-10-017112010.1186/s40537-020-00358-xReal-time monitoring of traffic parametersKirill Khazukov0Vladimir Shepelev1Tatiana Karpeta2Salavat Shabiev3Ivan Slobodin4Irakli Charbadze5Irina Alferova6South Ural State UniversitySouth Ural State UniversitySouth Ural State UniversitySouth Ural State UniversitySouth Ural State UniversitySouth Ural State UniversitySouth Ural State UniversityAbstract This study deals with the problem of rea-time obtaining quality data on the road traffic parameters based on the static street video surveillance camera data. The existing road traffic monitoring solutions are based on the use of traffic cameras located directly above the carriageways, which allows one to obtain fragmentary data on the speed and movement pattern of vehicles. The purpose of the study is to develop a system of high-quality and complete collection of real-time data, such as traffic flow intensity, driving directions, and average vehicle speed. At the same time, the data is collected within the entire functional area of intersections and adjacent road sections, which fall within the street video surveillance camera angle. Our solution is based on the use of the YOLOv3 neural network architecture and SORT open-source tracker. To train the neural network, we marked 6000 images and performed augmentation, which allowed us to form a dataset of 4.3 million vehicles. The basic performance of YOLO was improved using an additional mask branch and optimizing the shape of anchors. To determine the vehicle speed, we used a method of perspective transformation of coordinates from the original image to geographical coordinates. Testing of the system at night and in the daytime at six intersections showed the absolute percentage accuracy of vehicle counting, of no less than 92%. The error in determining the vehicle speed by the projection method, taking into account the camera calibration, did not exceed 1.5 km/h.http://link.springer.com/article/10.1186/s40537-020-00358-xNeural networkYOLO v3Data for training the neural network (Dataset)Traffic flow assessmentVehicle detectionVehicle classification |
| spellingShingle | Kirill Khazukov Vladimir Shepelev Tatiana Karpeta Salavat Shabiev Ivan Slobodin Irakli Charbadze Irina Alferova Real-time monitoring of traffic parameters Journal of Big Data Neural network YOLO v3 Data for training the neural network (Dataset) Traffic flow assessment Vehicle detection Vehicle classification |
| title | Real-time monitoring of traffic parameters |
| title_full | Real-time monitoring of traffic parameters |
| title_fullStr | Real-time monitoring of traffic parameters |
| title_full_unstemmed | Real-time monitoring of traffic parameters |
| title_short | Real-time monitoring of traffic parameters |
| title_sort | real time monitoring of traffic parameters |
| topic | Neural network YOLO v3 Data for training the neural network (Dataset) Traffic flow assessment Vehicle detection Vehicle classification |
| url | http://link.springer.com/article/10.1186/s40537-020-00358-x |
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