Traffic flow estimation with data from a video surveillance camera
Open accessAbstract
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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A CNN can find the location of a moving car in a traffic system, predict the car's forward path, and optimize crossroad throughput.
“For example, the CNN finds the location of a moving car in a traffic system, predicts the forward path of the car, and optimizes the crossroad throughput [28,30].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [28]
The Tracking module describes locating and tracking vehicles, then classifying their observed movement direction; it does not describe predicting a car’s future path. The Conclusions say city authorities will use the system’s results to optimize crossroad throughput, not that the CNN itself performs that optimization. (LLM, conf=0.88)
Experimental — accuracy not yet measuredSpecifying an ROI retains the quality of the most pertinent information in a transmission and reduces the amount of data transmitted, which increases the efficiency and bandwidth of a network utility.
“Specifying an ROI retains the quality of the most pertinent information in a transmission and reduces the amount of data transmitted, which increases the efficiency and bandwidth of a network utility [28][29][30][31][32].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [28]
The Adaptive feature pooling section (page unavailable) discusses ROI features used for vehicle detection, not specifying an ROI for transmission. The Dataset section mentions video compression artefacts, but the copy does not address whether ROI selection preserves pertinent information, reduces transmitted data, or improves network bandwidth (searched: ROI transmission, compression, bandwidth, transmitted). (LLM, conf=0.75)
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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
- Venue
- Journal Of Big Data
- Published
- 2019-08-07
- Volume
- 6
- Issue
- 1
- ISSN
- 2196-1115 (ISSN-L)
- 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
- 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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