Adversarially Learned Abnormal Trajectory Classifier

Nowadays, the collection of user data is increasing exponentially. In this paper, we propose the idea of using an adversarial With this huge amount of data, many end users network which basically transforms a one-class deep struggle to find the most efficient way of interpreting it. One autoencoder (DAE), like the one used in [6], which learns of the most challenging tasks is to learn and detect unusual solely from normal data, into a two-class network that information patterns from the observed data. This kind of can classify normal and abnormal trajectories without the information can be thought as any form of observations need of setting manually a detection threshold. We use that do not follow the usual ones and that can also look a similar data structure and deep autoencoder model to suspicious. A popular application of anomaly detection is that of Roy and Bilodeau [5] proposed, but, instead of the detection of abnormal events in video surveillance [1]- computing the threshold value that separates normal from [4] in which the main purpose is to identify all the pixel abnormal data, we integrate the pretrained DAE into a deep groups that deviate from the ordinarily observed groups.