Few-shot learning for trajectory outlier detection with only normal trajectories

Abstract

Trajectory outlier detection has a profound impact on many real-world applications. Most existing methods, whether supervised or unsupervised, require adequate historical data as a prerequisite. However, due to the uneven distribution of the trajectories in space, the detection in some regions(e.g. rural areas) may suffer from data scarcity. Furthermore, because the occurrence of outliers is a small probability event, there are frequently no outlier trajectories in such limited data which makes it even worse. To handle such issues, we in this paper study a new problem, i.e. few-shot trajectory outlier detection with only normal trajectories. And we propose a novel model, named MetaTAD, which consists of a Multi-scale Encoder and an Ab-detection Meta Learner. The Multi-scale Encoder aims to learn the diverse features of trajectories for outlier detection, while the Ab-detection Meta Learner guides the model to detect with few labeled normal trajectories. Extensive experiments on two real taxi trajectory datasets show that MetaTAD achieves state-of-the-art performance compared with the baselines.