  Anomaly detection in surveillance videos has been recently gaining attention.
A challenging aspect of high-dimensional applications such as video
surveillance is continual learning. While current state-of-the-art deep
learning approaches perform well on existing public datasets, they fail to work
in a continual learning framework due to computational and storage issues.
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 continual
learning, which in turn significantly reduces the training complexity and
provides a mechanism for continually learning from recent data without
suffering from catastrophic forgetting. Our proposed algorithm leverages the
feature extraction power of neural network-based models for transfer learning,
and the continual learning capability of statistical detection methods.
