A Modular Learning Approach for Fish Counting and Measurement Using Stereo Baited Remote Underwater Video

Abstract

An approach is suggested for automating fish identification and measurement using stereo Baited Remote Underwater Video footage. Simple methods for identifying fish are not sufficient for measurement, since the snout and tail points must be found, and the stereo data should be incorporated to find a true measurement. We present a modular framework that ties together various approaches in order to develop a generalised system for automated fish detection. A method is also suggested for using machine learning to improve identification. Experimental results indicate the suitability of our approach.