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Found 4 papers, 1 papers with code
Context-Dependent Anomaly Detection for Low Altitude Traffic Surveillance
To the best of our knowledge, our method is the first contextual anomaly detection method for UAV-assisted aerial surveillance.
UAV-AdNet: Unsupervised Anomaly Detection using Deep Neural Networks for Aerial Surveillance
Anomaly detection is a key goal of autonomous surveillance systems that should be able to alert unusual observations.
AU-AIR: A Multi-modal Unmanned Aerial Vehicle Dataset for Low Altitude Traffic Surveillance
As a result of this, several aerial datasets have been introduced, including visual data with object annotations.
COSMO: Contextualized Scene Modeling with Boltzmann Machines
For this end, we introduce a hybrid version of BMs where relations and affordances are introduced with shared, tri-way connections into the model.
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