Feature Grouping-Based Outlier Detection Upon Streaming Trajectories

Outlier detection acts as one of the most important analysis tasks for trajectory stream. In stream scenarios, such properties as unlimitedness, time-varying evolutionary, sparsity, and skewness distribution of trajectories pose new challenges to outlier detection technique. Trajectory outlier detection techniques mainly focus on finding trajectory that is dissimilar to the majority of the others, which is based on the hypothesis that they are probably generated by a different mechanism. Most distance-based methods tend to utilize a function (e.g., weighted linear sum) to measure the similarity of two arbitrary objects provided that representative features have been extracted in advance. However, this kind of method is not tailored to identify the outlier which is close to its neighbors according to some features, but behaves significantly different from its neighbors in terms of the other features. To address this issue, we propose a feature grouping-based mechanism that divides all the features into two groups, where the first group (Similarity Feature) is used to find close neighbors and the second group (Difference Feature) is used to find outliers within the similar neighborhood. According to the feature differences among local adjacent objects in one or more time intervals, we present two outlier definitions, including local anomaly trajectory fragment (TF-outlier) and evolutionary anomaly moving object (MO-outlier ). We devise a basic solution and then an optimized algorithm to detect both types of outliers. Experimental results show that our proposal is both effective and efficient to detect outliers upon trajectory data streams.