Intelligent tuna recognition for fisheries monitoring

Integrated video camera systems have been installed on fishing boats to trial for fishery monitoring in some countries. Currently, substantial amount of video footage is manually analyzed off the boats after each trip. Automatic processing of the videos is important for saving time and manpower. In this paper, an intelligent tuna recognition method is proposed. The method includes four steps. Firstly, the video is pre-processed by suppressing fast moving objects such as human. Secondly, the color and texture features are extracted to describe tuna, deck and other objects. Thirdly, support vector machine and statistic shape model are employed to identity and recognize tuna. Finally, a prior-knowledge based post-processing method is used to refine the recognition result. The experiment has showed that the proposed method is accurate and robust in tuna recognition. The method can also be used for other fish recognition applications, benefiting fisheries monitoring by providing efficient and automatic fish recognition.