  In this paper, we propose a novel abnormal event detection method with
spatio-temporal adversarial networks (STAN). We devise a spatio-temporal
generator which synthesizes an inter-frame by considering spatio-temporal
characteristics with bidirectional ConvLSTM. A proposed spatio-temporal
discriminator determines whether an input sequence is real-normal or not with
3D convolutional layers. These two networks are trained in an adversarial way
to effectively encode spatio-temporal features of normal patterns. After the
learning, the generator and the discriminator can be independently used as
detectors, and deviations from the learned normal patterns are detected as
abnormalities. Experimental results show that the proposed method achieved
competitive performance compared to the state-of-the-art methods. Further, for
the interpretation, we visualize the location of abnormal events detected by
the proposed networks using a generator loss and discriminator gradients.
