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Smart multi-modal marine monitoring via visual analysis and data fusion

Authors: 

Dian Zhang, Edel O’Connor, Timothy Sullivan, Kevin McGuinness, Fiona Regan, Noel E. O’Connor

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
Estuaries and coastal areas contain increasingly exploited resources that need to be monitored, managed and protected efficiently and effectively. This requires access to eliable and timely data and management decisions must be based on analysis of collected data to avoid or limit negative impacts. Visually supported multi-modal sensing and data fusion offer attractive possibilities for such arduous tasks. In this paper, we demonstrate how an in-situ sensor network can be enhanced with the use of contextual image data. We assimilate and alter a state-of-the-art background modelling technique from the image processing domain in order to detect turbidity spikes in water quality sensor measurements automatically. We then combine this with visual sensing to identify abnormal events that are not caused by local activities. The system can potentially assist those charged with monitoring large scale ecosystems, combining real-time analytics with improved efficiency and effectiveness.
Conference Name: 
MAED 2013, ACMMM 2013
Digital Object Identifer (DOI): 
10.NA
Publication Date: 
21/08/2013
Conference Location: 
Spain
Research Group: 
Institution: 
Dublin City University (DCU)
Open access repository: 
Yes