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The detection of contextual anomalies is a challenging task for surveillancesince an observation can be considered anomalous or normal in a specificenvironmental context. An unmanned aerial vehicle (UAV) can utilize its aerialmonitoring capability and employ multiple sensors to gather contextualinformation about the environment and perform contextual anomaly detection. Inthis work, we introduce a deep neural network-based method (CADNet) to findpoint anomalies (i.e., single instance anomalous data) and contextual anomalies(i.e., context-specific abnormality) in an environment using a UAV. The methodis based on a variational autoencoder (VAE) with a context sub-network. Thecontext sub-network extracts contextual information regarding the environmentusing GPS and time data, then feeds it to the VAE to predict anomaliesconditioned on the context. To the best of our knowledge, our method is thefirst contextual anomaly detection method for UAV-assisted aerial surveillance.We evaluate our method on the AU-AIR dataset in a traffic surveillancescenario. Quantitative comparisons against several baselines demonstrate thesuperiority of our approach in the anomaly detection tasks. The codes and datawill be available at https://bozcani.github.io/cadnet.

Context-Dependent Anomaly Detection for Low Altitude Traffic Surveillance