Traffic flow estimation with data from a video surveillance camera
This study addresses the problem of traffic flow estimation based on the data from a video surveillance camera. Target problem here is formulated as counting and classifying vehicles by their driving direction. This subject area is in early development, and the focus of this work is only one of the...
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| Published in: | Journal of big data Vol. 6; no. 1; pp. 1 - 15 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Springer International Publishing
07.08.2019
Springer Nature B.V SpringerOpen |
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| ISSN: | 2196-1115, 2196-1115 |
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| Abstract | This study addresses the problem of traffic flow estimation based on the data from a video surveillance camera. Target problem here is formulated as counting and classifying vehicles by their driving direction. This subject area is in early development, and the focus of this work is only one of the busiest crossroads in city Chelyabinsk, Russia. To solve the posed problem, we employed the state-of-the-art Faster R-CNN two-stage detector together with SORT tracker. A simple regions-based heuristic algorithm was used to classify vehicles movement direction. The baseline performance of the Faster R-CNN was enhanced by several modifications: focal loss, adaptive feature pooling, additional mask branch, and anchors optimization. To train and evaluate detector, we gathered 982 video frames with more than 60,000 objects presented in various conditions. The experimental results show that the proposed system can count vehicles and classify their driving direction during weekday rush hours with mean absolute percentage error that is less than 10%. The dataset presented here might be further used by other researches as a challenging test or additional training data. |
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| AbstractList | This study addresses the problem of traffic flow estimation based on the data from a video surveillance camera. Target problem here is formulated as counting and classifying vehicles by their driving direction. This subject area is in early development, and the focus of this work is only one of the busiest crossroads in city Chelyabinsk, Russia. To solve the posed problem, we employed the state-of-the-art Faster R-CNN two-stage detector together with SORT tracker. A simple regions-based heuristic algorithm was used to classify vehicles movement direction. The baseline performance of the Faster R-CNN was enhanced by several modifications: focal loss, adaptive feature pooling, additional mask branch, and anchors optimization. To train and evaluate detector, we gathered 982 video frames with more than 60,000 objects presented in various conditions. The experimental results show that the proposed system can count vehicles and classify their driving direction during weekday rush hours with mean absolute percentage error that is less than 10%. The dataset presented here might be further used by other researches as a challenging test or additional training data. Abstract This study addresses the problem of traffic flow estimation based on the data from a video surveillance camera. Target problem here is formulated as counting and classifying vehicles by their driving direction. This subject area is in early development, and the focus of this work is only one of the busiest crossroads in city Chelyabinsk, Russia. To solve the posed problem, we employed the state-of-the-art Faster R-CNN two-stage detector together with SORT tracker. A simple regions-based heuristic algorithm was used to classify vehicles movement direction. The baseline performance of the Faster R-CNN was enhanced by several modifications: focal loss, adaptive feature pooling, additional mask branch, and anchors optimization. To train and evaluate detector, we gathered 982 video frames with more than 60,000 objects presented in various conditions. The experimental results show that the proposed system can count vehicles and classify their driving direction during weekday rush hours with mean absolute percentage error that is less than 10%. The dataset presented here might be further used by other researches as a challenging test or additional training data. |
| ArticleNumber | 73 |
| Author | Nikolskaia, Kseniia Minbaleev, Alexey Fedorov, Aleksandr Ivanov, Sergey Shepelev, Vladimir |
| Author_xml | – sequence: 1 givenname: Aleksandr orcidid: 0000-0002-8829-3447 surname: Fedorov fullname: Fedorov, Aleksandr email: canxes@mail.ru organization: South Ural State University – sequence: 2 givenname: Kseniia orcidid: 0000-0001-8335-2084 surname: Nikolskaia fullname: Nikolskaia, Kseniia organization: South Ural State University – sequence: 3 givenname: Sergey orcidid: 0000-0002-1008-6202 surname: Ivanov fullname: Ivanov, Sergey organization: South Ural State University – sequence: 4 givenname: Vladimir surname: Shepelev fullname: Shepelev, Vladimir organization: South Ural State University – sequence: 5 givenname: Alexey surname: Minbaleev fullname: Minbaleev, Alexey organization: The Institute of State and Law of The Russian Academy of Sciences |
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| Keywords | Vehicle detection Traffic analysis Traffic flow estimation Convolutional neural network Surveillance data |
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| SubjectTerms | Algorithms Anchors Big Data Cameras Classification Communications Engineering Computational Science and Engineering Computer Science Convolutional neural network Counting Data Data Mining and Knowledge Discovery Database Management Driving Electronic surveillance Estimation Heuristic Heuristic methods Information Storage and Retrieval Mathematical Applications in Computer Science Methodology Networks Optimization Surveillance Surveillance data Traffic Traffic analysis Traffic flow Traffic flow estimation Traffic surveillance Vehicle detection Vehicles Video data |
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| Title | Traffic flow estimation with data from a video surveillance camera |
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