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Found 5 papers, 4 papers with code
Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles
A robust object detection is crucial for reliable results, hence the state-of-the-art deep neural network Mask-RCNN is applied for that purpose.
Accuracy Characterization of the Vehicle State Estimation from Aerial Imagery
With these error reductions, camera-equipped UAVs are very attractive tools for traffic data acquisition.
Signal Processing
Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios
Machine learning models are useful for scenario classification but most of them assume that data received during the testing are from one of the classes used in the training.
Unsupervised and Supervised Learning with the Random Forest Algorithm for Traffic Scenario Clustering and Classification
In the third part, a Random Forest classifier is trained using the defined clusters for the operational phase.
An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization
A modification of the Random Forest algorithm for the categorization of traffic situations is introduced in this paper.
