An Improved Top-Eye Trajectory Anomaly Detection Method Integrating Semantic Features
Yan Zhou, Cong Zhang, Mengdou Qin, Yeting Zhang, Jiaqi Wang · IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium · 2022
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.