Learning Normal Patterns via Adversarial Attention-Based Autoencoder for Abnormal Event Detection in Videos

Automatically detecting anomalies in videos is a challenging problem due to non-deterministic definitions of abnormal events and lack of sufficient training data. To address these issues, we propose an autoencoder coupled with attention model to discover normal patterns in videos via adversarial learning. Abnormal events are detected by diverging them from the normal patterns with the reconstruction error produced by the autoencoder. To this end, we build an end-to-end trainable adversarial attention-based autoencoder network, called Ada-Net, to make the reconstructed frames indistinguishable from original frames. The Ada-Net combines an autoencoder network and a GAN model that is used to benefit enhancing the reconstruction ability of the autoencoder. To further improve the reconstruction performance, we integrate an attention model into the decoder to dynamically select informative parts of encoding features for decoding. The attenion mechanism is helpful to preserving important information for learning intrinsic normal patterns. Evaluations on four challenging datasets, including the Subway, the UCSD Pedestrian, the CUHK Avenue, and the ShanghaiTech datasets, demonstrate the effectiveness of the proposed method.