Spatio-temporal event detection using dynamic conditional random fields
IJCAI(2009)
摘要
Event detection is a critical task in sensor networks for a variety of real-world applications. Many real-world events often exhibit complex spatio-temporal patterns whereby they manifest themselves via observations over time and space proximities. These spatio-temporal events cannot be handled well by many of the previous approaches. In this paper, we propose a new Spatio-Temporal Event Detection (STED) algorithm in sensor networks based on a dynamic conditional random field (DCRF) model. Our STED method handles the uncertainty of sensor data explicitly and permits neighborhood interactions in both observations and event labels. Experiments on both real data and synthetic data demonstrate that our STED method can provide accurate event detection in near real time even for large-scale sensor networks.
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关键词
large-scale sensor network,event detection,dynamic conditional random field,real-world event,accurate event detection,event label,sensor network,sensor data,spatio-temporal event detection,sted method,spatio-temporal event,synthetic data,near real time,conditional random field
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