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Consistent vehicle trajectory extraction from aerial recordings using oriented object detection.

Kevin Riehl1, Shaimaa K El-Baklish2, Anastasios Kouvelas2

  • 1Traffic Engineering Group, Institute for Transport Planning and Systems, ETH Zurich, Stefano-Franscini-Platz 5, 8093, Zurich, Switzerland. kriehl@ethz.ch.

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This summary is machine-generated.

This study introduces a new method for extracting vehicle trajectories from aerial videos using angular information. This approach improves trajectory accuracy and consistency, enhancing traffic flow analysis.

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Area of Science:

  • Computer Vision
  • Transportation Engineering
  • Artificial Intelligence

Background:

  • Vehicle trajectory extraction from aerial videos is crucial for traffic analysis.
  • Current methods using horizontal bounding boxes struggle with rotated vehicles and dense traffic.
  • Object detection using neural networks is a key component in trajectory extraction.

Purpose of the Study:

  • To propose a generalizable computation pipeline for high-quality vehicle trajectory extraction from aerial videos.
  • To leverage angular information and oriented object detection for improved trajectory accuracy.
  • To enhance the physical consistency and usability of reconstructed trajectories for traffic studies.

Main Methods:

  • Developed a computation pipeline utilizing angular information for trajectory extraction.
  • Designed a vehicle- and driver-informed trajectory reconstruction algorithm.
  • Evaluated 18 object detection models on a real-world video dataset.

Main Results:

  • Oriented object detection significantly improves trajectory consistency (15% internal, 20% platoon).
  • Angular information enhances the quality of reconstructed trajectories in Cartesian and lane coordinates.
  • Reconstructed trajectories better capture car-following and traffic dynamics.

Conclusions:

  • The proposed pipeline offers a significant improvement over traditional methods for vehicle trajectory extraction.
  • Leveraging angular information and oriented object detection enhances the accuracy and consistency of traffic analysis.
  • The enhanced trajectories improve the usability for detailed traffic flow studies and understanding vehicle dynamics.