  Anomaly detection in surveillance videos is attracting an increasing amount
of attention. Despite the competitive performance of recent methods, they lack
theoretical performance analysis, particularly due to the complex deep neural
network architectures used in decision making. Additionally, online decision
making is an important but mostly neglected factor in this domain. Much of the
existing methods that claim to be online, depend on batch or offline processing
in practice. Motivated by these research gaps, we propose an online anomaly
detection method in surveillance videos with asymptotic bounds on the false
alarm rate, which in turn provides a clear procedure for selecting a proper
decision threshold that satisfies the desired false alarm rate. Our proposed
algorithm consists of a multi-objective deep learning module along with a
statistical anomaly detection module, and its effectiveness is demonstrated on
several publicly available data sets where we outperform the state-of-the-art
algorithms. All codes are available at
https://github.com/kevaldoshi17/Prediction-based-Video-Anomaly-Detection-.
