Low-cost and high-performance abnormal trajectory detection based on the GRU model with deep spatiotemporal sequence analysis in cloud computing

Low-cost and high-performance abnormal trajectory detection based on the GRU model with deep spatiotemporal sequence analysis in cloud computing

Abstract Trajectory anomalies serve as early indicators of potential issues and frequently provide valuable insights into event occurrence. Existing methods for detecting abnormal trajectories primarily focus on comparing the spatial characteristics of the trajectories. However, they fail to capture...

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Bibliographic Details
Main Authors: Guohao Tang, Huaying Zhao, Baohua Yu
Format: Article
Language:English
Published: SpringerOpen 2024-03-01
Series:Journal of Cloud Computing: Advances, Systems and Applications
Subjects:
Trajectory anomalies
Anomaly detection
Deep spatiotemporal sequence analysis
Improved GRU
Online Access:https://doi.org/10.1186/s13677-024-00611-1

Internet

https://doi.org/10.1186/s13677-024-00611-1

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