An Improved Top-Eye Trajectory Anomaly Detection Method Integrating Semantic Features.
IEEE International Geoscience and Remote Sensing Symposium (IGARSS)(2022)
摘要
Anomalous trajectory detection is an important research content in the field of trajectory data mining. The anomaly detection method based on Top-k evolving trajectory outlier detection (TOP-EYE) is an effective anomaly detection method for moving objects that adopts evolutionary computation to detect anomalies from two perspectives of density and direction. However, the TOP-EYE method only considers two factors of density and direction, ignoring the multi-dimensional semantic features of trajectory data. In order to improve the accuracy of anomaly detection of trajectory data, this paper proposes an improved TOP-EYE method integrating semantic features, which adds trajectory semantic features for anomaly trajectory detection based on TOP-EYE. Experiments show that the proposed improved method integrating semantic features can more accurately identify anomalous trajectories than TOP-EYE, which is helpful for mining potential trajectory outliers with anomalous semantic features.
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关键词
trajectory anomaly detection,evolutionary computing,semantic feature
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