Skeleton-Based 3D Tracking of Multiple Fish From Two Orthogonal Views

This paper proposes a skeleton-based method for tracking multiple fish in 3D space. First, skeleton analysis is performed to simplify object into feature point representation according to shape characteristics of fish. Next, based on the obtained feature points, object association and matching are achieved to acquire the motion trajectories of fish in 3D space. This process relies on top-view tracking that is supplemented by side-view detection. While fully exploiting the shape and motion information of fish, the proposed method is able to solve the problems of frequent occlusions that occur during the tracking process and has good tracking performance.