Fish detection for species recognition

Abstract

Counting and tracking fish populations is important for conservation purposes as well as for the fishing industry. The fish counting typically occurs in rivers where the passing fish are counted either manually or automatically. Various automatic fish counters exist, based on such principles as resistivity, light beams and sonar. However, such methods typically cannot make distinction between fish and other passing objects, and moreover, cannot recognize different species. Computer vision techniques provide an attractive alternative for building a more robust and versatile fish counting systems. In this work the fish detection system, which provides the fish characterization for recognition purposes, was proposed. The results showed that by choosing an appropriate background subtraction method, it is possible to achieve a satisfying detection accuracy of 80% and 60% for two used datasets.