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Traffic data collection from aerial imagery
Abstract
This paper describes a new data collection system prototype for determining individual vehicle trajectories from sequences of digital aerial images. The software was tested on data collected from a helicopter, using a digital camera gathering high-resolution monochrome images. From the test, it is concluded that the techniques for analyzing the digital images can be applied automatically without much problems: 98% of the vehicles could be detected and tracked automatically when conditions were reasonable; this number lowered to 90% after the weather conditions worsened. This percentage will increase substantially under better weather conditions. Furthermore, equipment stabilizing the camera-so-called gyroscopic mounting-and the use of color images can be applied to further improve the system.
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“However, the methods are not efficient enough in remote sensing data because motion is also detected in pixels other than the targeted objects [7].”
From Vehicle Detection and Classification via YOLOv8 and Deep Belief Network over Aerial Image Sequences (2023) · cites this paper as [7]
Citations
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References 6 · Cited by 17
Research works printed but not linked: 7
- dijker, 1997, Verkeersafwikkeling bij congestie
- cassidy, 1999, Observations at a freeway bottleneck Proceedings 14th International Symposium of Transportation and Traffic Theory
- hoogendoom, 1999, Expert views on traffic flow operations during congestion, Re-search Report on behalf of the Traffic Research Centre of the Dutch Ministry of Transport
- herman, 1963, Car-following and Steady-State Flow, Theory of Traffic Flow Sym-posium Proceedings, 1
- kerner, 1999, Theory of Congested Traffic Flow: Self-Organization without Bottlenecks, Proceedings of the 14th International Symposium of Transportation and Traffic Theory, 147
- brackstone, 1998, Modeling of motorway operations – Transportation Research Records 1485
- brackstone, 1995, The microscopic modelling of traffic flow: weaknesses and potential developments, Traffic and Granular Flow
Metadata
- Authors
- Schreuder, M., Hoogendoorn, S. P., Van Zulyen, H. J., Gorte, B., & Vosselman, G.
- Affiliations
- Delft University of Technology (NL)
- Venue
- Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems
- Publisher
- IEEE
- Published
- 2004-04-23
- OpenAlex
- W1600502249
- Cited by
- 17
- References
- 6
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 2 · 2022: 1 · 2019: 2 · 2016: 2 · 2015: 1 · 2014: 1 · 2013: 2 · 2012: 1
- FWCI
- 0.50
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Intelligent Transportation Systems (ITSC) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Keywords
- traffic data collection, aerial imagery, vehicle trajectory estimation, vehicle detection, vehicle tracking, helicopter imagery, image sequences, color images
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- CoChat. (2026). Citation check: Traffic data collection from aerial imagery. https://app.cochat.ai/papers/10.1109/itsc.2003.1252056
- The paper
- Schreuder, M., Hoogendoorn, S. P., Van Zulyen, H. J., Gorte, B., & Vosselman, G. (2004). Traffic data collection from aerial imagery. Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems. https://doi.org/10.1109/itsc.2003.1252056
