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Abstract

Modern fisheries management for scallop fisheries requires accurate assessment of fish stocks and careful monitoring of marine habitats. Underwater video promises to provide a means of obtaining this information in a cost effective and non-destructive way, but a major limitation is the need for time-consuming and subjective manual analysis of the video data obtained. This poster reports on research, conducted in conjunction with the Tasmanian Aquaculture and Fisheries Institute, focusses on the development of computer vision techniques to automate the process of counting scallops depicted in video image sequences. The techniques will locate objects of interest, analyse the objects to determine which are scallops and then identify the scallop species

Item Type:	Conference or Workshop Item (Poster)
Authors/Creators:	Fearn, RC and Williams, R and Cameron-Jones, RM
Keywords:	artificial intelligence, automated video analysis, scallops stocks assessment, fisheries science, computer vision
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