Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks

Understand

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.