Automatic Fish Recognition and Counting in Video Footage of Fishery Operations

This paper presents an accurate and automatic algorithm to recognize and count fish in the video footages of fishery operations. The unique character of the approach is that it combines machine learning techniques with statistical methods to fully make use the benefits of these algorithms. The approach consists of three major stages including video data preparation such as noise deduction, preliminary fish recognition with artificial neural network to classify image areas into either fish or non-fish, and fine fish recognition and counting with statistical shape models. Experiment results of tuna recognition and counting using the proposed method are presented with performance validation and discussion.