Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles

@article{Kruber2020VehiclePE,
  title={Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles},
  author={Friedrich Kruber and Eduardo S'anchez Morales and S. Chakraborty and M. Botsch},
  journal={2020 IEEE Intelligent Vehicles Symposium (IV)},
  year={2020},
  pages={2089-2096}
}
The availability of real-world data is a key element for novel developments in the fields of automotive and traffic research. Aerial imagery has the major advantage of recording multiple objects simultaneously and overcomes limitations such as occlusions. However, there are only few data sets available. This work describes a process to estimate a precise vehicle position from aerial imagery. A robust object detection is crucial for reliable results, hence the state-of-the-art deep neural… Expand

References

SHOWING 1-10 OF 29 REFERENCES
Accuracy Characterization of the Vehicle State Estimation from Aerial Imagery
R3-Net: A Deep Network for Multioriented Vehicle Detection in Aerial Images and Videos
DroNet: Efficient convolutional neural network detector for real-time UAV applications
Vision meets robotics: The KITTI dataset
The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems
Vehicle Instance Segmentation From Aerial Image and Video Using a Multitask Learning Residual Fully Convolutional Network
  • Lichao Mou, X. Zhu
  • Computer Science
  • IEEE Transactions on Geoscience and Remote Sensing
  • 2018
MLESAC: A New Robust Estimator with Application to Estimating Image Geometry
Parallel Multi-Hypothesis Algorithm for Criticality Estimation in Traffic and Collision Avoidance
...
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2
3
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