Automated intelligent abundance analysis of scallop survey video footage

Authors:
Rob Fearn;Raymond Williams;Mike Cameron-Jones;Julian Harrington;Jayson Semmens
Affiliations:
School of Computing, University of Tasmania, Tasmania, Australia;School of Computing, University of Tasmania, Tasmania, Australia;School of Computing, University of Tasmania, Tasmania, Australia;Marine Research Laboratories, Tasmanian Aquaculture and Fisheries Institute, Tasmania, Australia;Marine Research Laboratories, Tasmanian Aquaculture and Fisheries Institute, Tasmania, Australia
Venue:
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Year:
2007

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Abstract

Underwater video is increasingly being pursued as a low impact alternative to traditional techniques (such as trawls and dredges) for determining abundance and size frequency of target species. Our research focuses on automatically annotating survey scallop video footage using artificial intelligence techniques. We use a multi-layered approach which implements an attention selection process followed by sub-image segmentation and classification. Initial attention selection is performed using the University of Southern California's (USCs) iLab Neuromorphic Visual Toolkit (iNVT). Once the iNVT has determined regions of potential interest we use image segmentation and feature extraction techniques to produce data suitable for analysis within the Weka machine learning workbench environment.