Trajectory Mining from VMS Data for Identifying Fishing Tackles

Automatic identification of fishing equipment has a big impact on fisheries managements and illegal fishing surveillance. For many years, existing approaches to recognize fishing gear types have been proposed based on analysis of Vessel Monitoring System (VMS) data. However, the ship tracking data typically contain irrelevant and meaningless information that can limit their effectiveness. An innovative approach present in this paper is to identify types of fishing equipment from VMS records. Our approach first tries to identify activities of interest in a fishing using an unsupervised way. It then generates possible trajectories for the local movements and performs feature extraction. Two types of trajectory-based features are extracted to describe both global and local characteristics of fishing movement patterns. We finally perform dimension reduction and build the classifier using machine learning. Experiments conducted on historical VMS records from 180 commercial fishing boats with three major types of fishing gears in Thailand show that our approach achieves encouraging performance of recognition rates.