  Anomaly detection in videos aims at reporting anything that does not conform
the normal behaviour or distribution. However, due to the sparsity of abnormal
video clips in real life, collecting annotated data for supervised learning is
exceptionally cumbersome. Inspired by the practicability of generative models
for semi-supervised learning, we propose a novel sequential generative model
based on variational autoencoder (VAE) for future frame prediction with
convolutional LSTM (ConvLSTM). To the best of our knowledge, this is the first
work that considers temporal information in future frame prediction based
anomaly detection framework from the model perspective. Our experiments
demonstrate that our approach is superior to the state-of-the-art methods on
three benchmark datasets.
