  We address the problem of abnormal event detection from trajectory data. In
this paper, a new adversarial approach is proposed for building a deep neural
network binary classifier, trained in an unsupervised fashion, that can
distinguish normal from abnormal trajectory-based events without the need for
setting manual detection threshold. Inspired by the generative adversarial
network (GAN) framework, our GAN version is a discriminative one in which the
discriminator is trained to distinguish normal and abnormal trajectory
reconstruction errors given by a deep autoencoder. With urban traffic videos
and their associated trajectories, our proposed method gives the best accuracy
for abnormal trajectory detection. In addition, our model can easily be
generalized for abnormal trajectory-based event detection and can still yield
the best behavioural detection results as demonstrated on the CAVIAR dataset.
