Detecting moving object outliers in massive-scale trajectory streams

Abstract

The detection of abnormal moving objects over high-volume trajectory streams is critical for real time applications ranging from military surveillance to transportation management. Yet this problem remains largely unexplored. In this work, we first propose classes of novel trajectory outlier definitions that model the anomalous behavior of moving objects for a large range of real time applications. Our theoretical analysis and empirical study on the Beijing Taxi and GMTI (Ground Moving Target Indicator) datasets demonstrate its effectiveness in capturing abnormal moving objects. Furthermore we propose a general strategy for efficiently detecting the new outlier classes. It features three fundamental optimization principles designed to minimize the detection costs. Our comprehensive experimental studies demonstrate that our proposed strategy drives the detection costs 100-fold down into practical realm for applications producing high volume trajectory streams to utilize.