Cited by 149Video Surveillance and Tracking MethodsAdvanced Neural Network ApplicationsAutonomous Vehicle Technology and Safety

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Traffic flow estimation with data from a video surveillance camera

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Fedorov, Aleksandr, Nikolskaia, Kseniia, Ivanov, Sergey, Shepelev, Vladimir, Minbaleev, Alexey·2019·Journal Of Big Data

DOI 10.1186/s40537-019-0234-z

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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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Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
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Authors: Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., & Minbaleev, A.
Affiliations: South Ural State University (RU)
Corresponding author: Fedorov, Aleksandr
ORCID: Fedorov, Aleksandr
Nikolskaia, Kseniia
Ivanov, Sergey
Shepelev, Vladimir
Venue: Journal Of Big Data
Publisher: Springer Science and Business Media LLC
Published: 2019-08-07
Volume: 6
Issue: 1
ISSN: 2196-1115 (ISSN-L)
DOI: 10.1186/s40537-019-0234-z
OpenAlex: W2964933785
Cited by: 149
References: 45
Citations by year: 2026: 8 · 2025: 25 · 2024: 25 · 2023: 16 · 2022: 26 · 2021: 27 · 2020: 22
FWCI: 8.40
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Journal Of Big Data (publishedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: Government of the Russian Federation (02.A03.21.0011)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
Keywords: traffic flow estimation, vehicle counting, video surveillance, Faster R-CNN, object detection, focal loss, crossroads, Chelyabinsk

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Traffic flow estimation with data from a video surveillance camera

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