Trajectory Outlier Detection: A Partition-and-Detect Framework
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Temporal Outlier Detection in Vehicle Traffic Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Efficient anomaly monitoring over moving object trajectory streams
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
A taxi business intelligence system
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
iBAT: detecting anomalous taxi trajectories from GPS traces
Proceedings of the 13th international conference on Ubiquitous computing
Integrating community matching and outlier detection for mining evolutionary community outliers
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Community trend outlier detection using soft temporal pattern mining
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Real Time Anomalous Trajectory Detection and Analysis
Mobile Networks and Applications
TODMIS: mining communities from trajectories
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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The increasing availability of large-scale location traces creates unprecedent opportunities to change the paradigm for identifying abnormal moving activities. Indeed, various aspects of abnormality of moving patterns have recently been exploited, such as wrong direction and wandering. However, there is no recognized way of combining different aspects into an unified evolving abnormality score which has the ability to capture the evolving nature of abnormal moving trajectories. To that end, in this paper, we provide an evolving trajectory outlier detection method, named TOP-EYE, which continuously computes the outlying score for each trajectory in an accumulating way. Specifically, in TOP-EYE, we introduce a decay function to mitigate the influence of the past trajectories on the evolving outlying score, which is defined based on the evolving moving direction and density of trajectories. This decay function enables the evolving computation of accumulated outlying scores along the trajectories. An advantage of TOP-EYE is to identify evolving outliers at very early stage with relatively low false alarm rate. Finally, experimental results on real-world location traces show that TOP-EYE can effectively capture evolving abnormal trajectories.