Tracking Full-Body Motion of Multiple Fish with Midline Subspace Constrained Multicue Optimization

Capturing the body motion of fish has been gaining considerable attention from scientists of various fields. In this paper, we propose a method which is able to track the full-body motion of multiple fish with frequent interactions. We firstly propose to model the midline subspace of a fish body which gives a compact low-dimensional representation of the complex shape and motion. Then we propose a particle swarm-based optimization framework whose objective function takes into account multiple sources of information. The proposed multicue objective function is able to describe the details of fish appearance and is also effective through mutual occlusions. Excessive experimental results have demonstrated the effectiveness and robustness of the proposed method.