  In this paper, we focus on the development of a method that detects abnormal
trajectories of road users at traffic intersections. The main difficulty with
this is the fact that there are very few abnormal data and the normal ones are
insufficient for the training of any kinds of machine learning model. To tackle
these problems, we proposed the solution of using a deep autoencoder network
trained solely through augmented data considered as normal. By generating
artificial abnormal trajectories, our method is tested on four different
outdoor urban users scenes and performs better compared to some classical
outlier detection methods.
