Anomaly detection using prediction error with Spatio-Temporal Convolutional LSTM
This work addresses anomaly detection in videos, which is important for security and surveillance applications, but it is incremental as it builds on existing sequence-to-sequence architectures.
The paper tackled video anomaly detection by proposing a method using prediction error with a spatio-temporal convolutional LSTM, showing that prediction outperforms reconstruction on five benchmark datasets and achieves results comparable to state-of-the-art.
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.