Video Latent Code Interpolation for Anomalous Behavior Detection

Detecting an anomalous human behavior can be a challenging task. In this paper, we present a novel objective function for autoencoders which include a temporal component. Our method is a fully end-to-end semi-supervised approach for video anomaly detection. The autoencoder is trained to reconstruct a sample from a partial input, by interpolating latent codes obtained from this partial input. We show this approach improves over using usual autoencoder objective functions for video anomaly detection and achieves results close to the state of the art on a broad range of datasets. Our code is publicly available on github.