  Anomaly detection in surveillance videos has been recently gaining attention.
Even though the performance of state-of-the-art methods on publicly available
data sets has been competitive, they demand a massive amount of training data.
Also, they lack a concrete approach for continuously updating the trained model
once new data is available. Furthermore, online decision making is an important
but mostly neglected factor in this domain. Motivated by these research gaps,
we propose an online anomaly detection method for surveillance videos using
transfer learning and any-shot learning, which in turn significantly reduces
the training complexity and provides a mechanism that can detect anomalies
using only a few labeled nominal examples. Our proposed algorithm leverages the
feature extraction power of neural network-based models for transfer learning
and the any-shot learning capability of statistical detection methods.
