Convolutional Neural Networks for Counting Fish in Fisheries Surveillance Video_French, et al.
Abstract: We present a computer vision tool that analyses video from a CCTV system installed on fishing trawlers to monitor discarded fish catch. The system aims to support expert observers who review the footage and verify numbers, species and sizes of discarded
fish. The operational environment presents a significant challenge for these tasks. Fish
are processed below deck under fluorescent lights, they are randomly oriented and there
are multiple occlusions. The scene is unstructured and complicated by the presence of
fishermen processing the catch. We describe an approach to segmenting the scene and
counting fish that exploits the N4-Fields algorithm. We performed extensive tests of the
algorithm on a data set comprising 443 frames from 6 belts. Results indicate the relative
count error (for individual fish) ranges from 2% to 16%. We believe this is the first
system that is able to handle footage from operational trawlers.
