Fusion of Static and Temporal Information for Threat Evaluation in Sensor Networks


In many CCTV and sensor network based intelligent surveillance systems, a number of attributes or criteria are used to individually evaluate the degree of potential threat of a suspect. The outcomes for these attributes are in general from analytical algorithms where data are often pervaded with uncertainty and incompleteness. As a result, such individual threat evaluations are often inconsistent, and individual evaluations can change as time elapses. Therefore, integrating heterogeneous threat evaluations with temporal influence to obtain a better overall evaluation is a challenging issue. So far, this issue has rarely be considered by existing event reasoning frameworks under uncertainty in sensor network based surveillance. In this paper, we first propose a weighted aggregation operator based on a set of principles that constraints the fusion of individual threat evaluations. Then, we propose a method to integrate the temporal influence on threat evaluation changes. Finally, we demonstrate the usefulness of our system with a decision support event modeling framework using an airport security surveillance scenario.

DOI: 10.1007/978-3-319-25159-2_6

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@inproceedings{Ma2015FusionOS, title={Fusion of Static and Temporal Information for Threat Evaluation in Sensor Networks}, author={Wenjun Ma and Weiru Liu and Jun Hong}, booktitle={KSEM}, year={2015} }