  In this paper, we propose a novel method for video anomaly detection
motivated by an existing architecture for sequence-to-sequence prediction and
reconstruction using a spatio-temporal convolutional Long Short-Term Memory
(convLSTM). As in previous work on anomaly detection, anomalies arise as
spatially localised failures in reconstruction or prediction. In experiments
with five benchmark datasets, we show that using prediction gives superior
performance to using reconstruction. We also compare performance with different
length input/output sequences. Overall, our results using prediction are
comparable with the state of the art on the benchmark datasets.
