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