Spatio-temporal event detection using dynamic conditional random fields
Jie Yin, Derek Hao Hu, Qiang Yang · 2009
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 ob-servations 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 Detec-tion (STED) algorithm in sensor networks based on a dynamic conditional randomfield (DCRF) model. Our STED method handles the uncertainty of sen-sor data explicitly and permits neighborhood in-teractions in both observations and event labels. Experiments on both real data and synthetic data demonstrate that our STED method can provide ac-curate event detection in near real time even for large-scale sensor networks. 1