Spatio-temporal event detection using dynamic conditional random fields

Authors:
Jie Yin;Derek Hao Hu;Qiang Yang
Affiliations:
Information Engineering Laboratory, CSIRO ICT Centre, Australia;Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China;Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China
Venue:
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Year:
2009

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Abstract

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.