The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
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
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
(2016) · Cited by 31,590
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
SSD: Single Shot MultiBox Detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_2 · Cited by 22,128
Cited by
Sorted by citations
Items 101–149 of 149
Methodology for Predictive Estimation of Specific Greenhouse Gas Emissions by Traffic Flows
Trofimenko, Y. V., Deianov, D. A., Иванович, К. В., & Fedotov, N. N. (2022) · Intelligent Technologies and Electronic Devices in Vehicle and Road Transport Complex (TIRVED) · doi:10.1109/tirved56496.2022.9965512 · Cited by 2
Scale Invariant low frame rate tracking
Braga, A. P., Acchetta, É. C., & Rodrigues, P. S. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119366 · Cited by 2
Meicler, A., Sanogo, A., Shvai, N., Llanza, A., Hasnat, A., Khata, M., Ed-doughmi, Y., Khalil, A., Lachachi, M. Y., & Nakib, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50347-5_10 · Cited by 2
A survey on the efficiency of the use of RFID technology in traffic management systems
Stăncel, I. N., & Surugiu, M. C. (2023) · IEEE International Symposium for Design and Technology in Electronic Packaging (SIITME) · doi:10.1109/siitme59799.2023.10431300 · Cited by 1
Real-Time Modeling of Traffic Flow and Emissions for Enhancing Urban Air Quality
Shepelev, V., Жанказиев, С., Fatikhova, L., Alferova, I., & Aitbayev, M. (2024) · Springer proceedings in earth and environmental sciences · doi:10.1007/978-3-031-64423-8_8 · Cited by 1
Adaptive Extrinsic Calibration of Roadside Cameras for Surveillance Tasks
Fleck, T., Burger, J., & Zöllner, J. M. (2025) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc60802.2025.11423640 · Cited by 1
Using Simulation for Investigating Emergency Traffic Situations
Makarova, I., Yakupova, G., Buyvol, P. A., Mukhametdinov, E. M., Abashev, A., & Mustafina, J. (2023) · International Conference on Developments in eSystems Engineering (DeSE) · doi:10.1109/dese58274.2023.10099681 · Cited by 1
Madhukaro, E. A. J. R. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i10.8620 · Cited by 1
Traffic Flow Forecasting for Road Projects Design in Burkina Faso
Gansonré, Y., Pani, A., & Breul, P. (2024) · Civil Engineering and Architecture · doi:10.13189/cea.2024.121335 · Cited by 1
RETRACTED: Modification of the Viola-Jones method for face tracking in a video stream
Lampezhev, A. K., Ruslantsev, A. N., Ivanov, N. N., & Gorelov, V. (2024) · BIO Web of Conferences · doi:10.1051/bioconf/202413802035 · Cited by 1
RetractedUnveiling Effectiveness: Advanced Vehicle Tracking and Detection Systems in Action
Aeri, M., & Purohit, K. C. (2024) · International Conference on Technological Advancements in Computational Sciences (ICTACS) · doi:10.1109/ictacs62700.2024.10840554 · Cited by 1
Object Detection Using Efficient Partitioning and Frame Reduction
Imran, O., Rajan, S., & Majumdar, S. (2025) · International Conference on Consumer Electronics · doi:10.1109/icce63647.2025.10929775 · Cited by 1
CLOUD-CODEC : A New Way of Storing Traffic Camera Footage at Scale
Kim, H., Khudoyberdiev, A., Garnaik, S. S. R., Bhattacharya, A., & Ryoo, J. (2025) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3744649 · Cited by 1
Robust Radio Communication Protocol for Traffic Analysis Application
Nikodem, M., Surmacz, T., Słabicki, M., Hofman, D., Klimkowski, P., & Dołęga, C. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-48256-5_45 · Cited by 1
Application for Recognition of Transport Using Pytorch Library
Lyzhin, D., Volodchenko, I., & Petrova, L. A. (2020) · International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon) · doi:10.1109/fareastcon50210.2020.9271215 · Cited by 1
PREDICTING AND MANAGING LANE CAPACITY AT A SIGNAL-CONTROLLED INTERSECTION
Shepelev, V., Gorodokin, V., Slobodin, I., & Khazukov, K. (2021) · Вестник Южно-Уральского государственного университета. Серия: Экономика и менеджмент · doi:10.14529/em210220 · Cited by 1
Traffic Density Dependent Fine Tuning of Green Signal Timing for Faster Commute
Shende, S., Abraham, J., & Matange, A. (2022) · International Conference on Industry 4.0 Technology (I4Tech) · doi:10.1109/i4tech55392.2022.9952867 · Cited by 1
Bakker‐Reynolds, G., Erturk, E., & Lengyel, I. (2021) · doi:10.1002/9781119748342.ch8 · Cited by 1
Application Development for Determining the Author of a Picture Based on Neural Network Technologies
Bardina, M., Volodchenko, I., & Sukhov, M. V. (2020) · International Russian Automation Conference (RusAutoCon) · doi:10.1109/rusautocon49822.2020.9208200
Development of an Application for Recognizing Automobile Vehicles
Kataev, G., Shabley, A. A., & Ваулин, С. Д. (2020) · International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon) · doi:10.1109/fareastcon50210.2020.9271500
Golovnin, O. K., Прохоров, С. А., & Stolbova, A. (2021) · Journal of Dynamics and Vibroacoustics · doi:10.18287/2409-4579-2020-6-4-35-39
Yang, I., & Jeon, W.-H. (2021) · Journal of Digital Contents Society · doi:10.9728/dcs.2021.22.5.831
SmartEye: Android Application for Traffic Monitoring
Firoz, K., Sulthan, F., Muhammed Shaik, S., Bezalel, V. M., & K, M. N. B. (2021)
New Directions in Traffic Control Analysis through Video Surveillance
Lalitha, R. V. S., Jalligampala, D. L. S., Kavitha, K., Vahida, S., & Rajasekhar, G. (2021) · E3S Web of Conferences · doi:10.1051/e3sconf/202130901099
Research on Traffic Video Flow Monitoring Based on Spark
Mu, J., Luan, J., & Li, D. (2021) · International Conference on Computer Science and Management Technology (ICCSMT) · doi:10.1109/iccsmt54525.2021.00068
SafeCross - VCA Based Road Traffic Regulations Compliance Detection
Szklarczyk, R., Nikodem, J., Fałat, P., Foks, K., Caputa, M., & Szklarczyk, A. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-031-23012-7_3
Construction of a System for Dynamic Monitoring of Pollutant Emissions by Transport Flows
Glushkov, A., Shepelev, V., Slobodin, I., Жанказиев, С., & Юрьевич, М. К. (2023) · Transportation research procedia · doi:10.1016/j.trpro.2023.02.074
Moving Object 3D Detection and Segmentation Using Optical Flow Clustering
Zhuravlev, D. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-35314-7_38
Effect of Dynamic Speed Limit on Level of Service For Ai-Guided Traffic AnalysisPreprint
Panda, A., Dogra, D. P., & Dey, P. P. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4898284
Modification of the Viola-Jones Algorithm for Video Face Identification
Gorelov, V. A., Muranov, A. N., Ivanov, N. Z., & Lampezhev, A. K. (2024) · Dynamics of Systems Mechanisms and Machines · doi:10.1109/dynamics64718.2024.10838652
A Location-Based Application for Recording the Video Stream of Urban Traffic Cameras
Leoste, K. A., Leoste, J. V., Leoste, J., & Martin, J. S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-97-5703-9_29
Automatic Traffic Surveillance Using Deep Learning Model Under Extreme Climatic Conditions
Venkataramani, S. G. (2025) · International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) · doi:10.1109/idciot64235.2025.10915125
Foreground–background separation and video reconstruction using moment matching
Bhattacharjee, D., Shakib, M. F., & Astolfi, A. (2025) · European Journal of Control · doi:10.1016/j.ejcon.2025.101291
Enhancing Fisheye Object Detection Using Frequency-Domain Attention and Dual Aggregation Transformer
Alade, O., Khan, N. F., & Aboah, A. (2025) · IEEE Access · doi:10.1109/access.2025.3596026
Guo, J., & Wang, S. (2025) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv64158.2025.11097401
Gupta, D., & Kumar, M. (2025) · Circuits Systems and Signal Processing · doi:10.1007/s00034-025-03295-3
Privacy-Aware Visual Memorability in Traffic Surveillance
Gao, Y., Bhattacharya, A., Jin, S., & Cho, S. (2025) · Workshop on Privacy in the Electronic Society · doi:10.1145/3733802.3764060
From Sensing to Impact: LiDAR-Based Parking Guidance for Narrow Streets in a Coastal Tourist Village
Hayauchi, G., Akagi, Y., Miwa, T., Matsui, Y., Kenmochi, C., & Morikawa, T. (2025) · International Journal of Intelligent Transportation Systems Research · doi:10.1007/s13177-025-00596-4
Pixel-to-Cyberphysical: Vision-Based Lightweight Digital Twin forIntelligent Transportation SystemsPreprint
Feng, M., He, C., Wang, X., Yang, L., Cai, Z., & Hu, P. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5991729
SLICE Street-Level Insights from Camera Evidence
Roch, P., Laaser, L., Nejad, B. S., Handte, M., & Marrón, P. J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-14495-9_21
Real-time Facial Recognition Systems: A Survey of Techniques, Applications and Challenges
Abdullah, M., & Khalil, M. (2025) · International Conference on Digital Futures and Transformative Technologies (ICoDT2) · doi:10.1109/icodt269104.2025.11360711
Rizk, M., Taleb, H., Rhayem, A., Chaaya, J. A., Zaki, C., & Nasser, A. (2026) · Research Square · doi:10.21203/rs.3.rs-8815873/v1
Liu, Y. F., & Zhang, J. (2026) · Future Generation Computer Systems · doi:10.1016/j.future.2026.108768
Feng, M., He, C., Wang, X., Yang, L., Hu, P., Cai, Z., Yan, Z., & Ma, J. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132914
Karthika, M. G., Cheranchery, M. F., & Harikrishna, M. (2026) · Journal of Transportation Engineering Part A Systems · doi:10.1061/jtepbs.teeng-9437
Providing massive scenario data for intelligent vehicles using road-side cameras
Chang, C., Liu, Q., Ge, J., Zhang, K., Zhang, J., Li, Z., Li, B., & Li, L. (2026) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2026.105845
Ivanov, I., Kotev, V., Ivanova, M., & Petkov, K. (2026) · International Conference on Engineering, Technologies and Systems (TechSys) · doi:10.3390/engproc2026150027
Recovering large-scale Urban traffic flow data from sparse observations via analytical optimization
Fu, S., Shi, H., Liang, S., Ma, W., & Ran, B. (2026) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1016/j.cacaie.2026.100180
Metadata
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
