The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Real-Time Traffic Analysis using Deep Learning Techniques and UAV based Video
Open accessAbstract
In urban environments there are daily issues of traffic congestion which city authorities need to address. Realtime analysis of traffic flow information is crucial for efficiently managing urban traffic. This paper aims to conduct traffic analysis using UAV-based videos and deep learning techniques. The road traffic video is collected by using a position-fixed UAV. The most recent deep learning methods are applied to identify the moving objects in videos. The relevant mobility metrics are calculated to conduct traffic analysis and measure the consequences of traffic congestion. The proposed approach is validated with the manual analysis results and the visualization results. The traffic analysis process is real-time in terms of the pre-trained model used.
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“Differently, some works have reported tracking performance by illustrating motion tracks, such as in [67], while others have only presented qualitative analysis, for instance in [68].”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [67]
In the citing paper
“[67] Faster R-CNN | CNN | Custom | -- | -- | -- | Two classes (Bus & Car) | -- | -- | 96.50 | --”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [67]
In the citing paper
“[67] | i. To count traffic flow ii. To find traffic jam | i. By drawing tracks ii. Frame-based counting | i. 92.7 ii. --”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [67]
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Research works printed but not linked: 12
- lin, 2014, Microsoft COCO: Common Objects in Context, Proc of ECC 2014, 740
- girshick, 2018, Detectron
- olawale, 2015, A Four-Step Ortho-Rectification Procedure for GeoReferencing Video Streams from a Low-Cost UAV, International Journal of Computer and Information Engineering, 9
- 10.1016/S0262-8856(97)00009-7 · DOI
- 10.1109/MCG.2010.64 · DOI
- 10.1371/journal.pone.0186098 · DOI
- 10.1109/ICIST.2014.6920557 · DOI
- 10.1109/TPAMI.2018.2844175 · DOI · Paper page
- garcia-garcia, 2017, A review on deep learning techniques applied to semantic segmentation
- 10.1038/ncomms10793 · DOI
- 2018, World Urbanization Prospects
- leo, 2018, Deep Learning for Assistive Computer Vision, LNCS, 11134, 3
Cited by
A Systematic Review of Drone Based Road Traffic Monitoring System
Bisio, I., Garibotto, C., Haleem, H., Lavagetto, F., & Sciarrone, A. (2022) · IEEE Access · doi:10.1109/access.2022.3207282 · Cited by 139
“Dif-456 ferently, some works have reported tracking performance by illustrating motion tracks, such as in [66], while others have only presented qualitative analysis, for instance in [67].”
BackgroundMethod
Metadata
- Authors
- Zhang, H., Liptrott, M., Bessis, N., & Cheng, J.
- Affiliations
- Edge Hill University (GB)
- Manchester Metropolitan University (GB)
- Venue
- 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
- Publisher
- IEEE
- Published
- 2019-09
- Pages
- 1-5
- OpenAlex
- W2989687024
- Cited by
- 52
- References
- 1
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 8 · 2023: 13 · 2022: 13 · 2021: 6 · 2020: 3
- FWCI
- 1.79
- Type
- conference paper
- Language
- en
- Open access
- Licence
- Locations
- IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (publishedVersion)
- e-space (Manchester Metropolitan University) (acceptedVersion)
- Edge Hill University Research Information Repository (Edge Hill University) (submittedVersion, cc-by-nc-nd)
- Figshare (submittedVersion)
- 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
- Social Sciences › Transportation › Traffic Prediction and Management Techniques
- Keywords
- real-time traffic analysis, traffic congestion, unmanned aerial vehicles, UAV video, object detection, moving object identification, mobility metrics, urban traffic management, traffic flow
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- CoChat. (2026). Citation check: Real-Time Traffic Analysis using Deep Learning Techniques and UAV based Video. https://app.cochat.ai/papers/10.1109/avss.2019.8909879
- The paper
- Zhang, H., Liptrott, M., Bessis, N., & Cheng, J. (2019). Real-Time Traffic Analysis using Deep Learning Techniques and UAV based Video. 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). https://doi.org/10.1109/avss.2019.8909879
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
