  Automating the detection of anomalous events within long video sequences is
challenging due to the ambiguity of how such events are defined. We approach
the problem by learning generative models that can identify anomalies in videos
using limited supervision. We propose end-to-end trainable composite
Convolutional Long Short-Term Memory (Conv-LSTM) networks that are able to
predict the evolution of a video sequence from a small number of input frames.
Regularity scores are derived from the reconstruction errors of a set of
predictions with abnormal video sequences yielding lower regularity scores as
they diverge further from the actual sequence over time. The models utilize a
composite structure and examine the effects of conditioning in learning more
meaningful representations. The best model is chosen based on the
reconstruction and prediction accuracy. The Conv-LSTM models are evaluated both
qualitatively and quantitatively, demonstrating competitive results on anomaly
detection datasets. Conv-LSTM units are shown to be an effective tool for
modeling and predicting video sequences.
