A Deep Learning Based Technique for Anomaly Detection in Surveillance Videos

Abstract

In this paper the problem of anomaly detection in surveillance videos is addressed, which refers to the detection of events that do not conform to normal behaviour. To solve this problem, this paper proposes an approach that utilizes a Deep Neural Network (DNN) to model normal behaviour. Specifically, a DNN is built that learns to predict future frames from past frames using a normal (anomaly free) dataset. The predictions from the model are then compared with testing video for similarity, and the resulting error is used to detect anomalies. Benchmarks of the proposed approach on two datasets common in the anomaly detection literature show that it performs comparably to other methods in the literature, even though it does not rely on any hand-crafted features. Moreover, comparison to other deep learning techniques in the literature shows that the proposed approach is significantly less complex.