3D tracking swimming fish school with learned kinematic model using LSTM network
Shuo Hong Wang, Jingwen Zhao, Xiang Liu, Zhiming Qian, Ye Liu, Yan Qiu Chen · 2017
This paper proposes a reliable 3D fish tracking method using a novel master-slave camera setup. Instead of conventional dynamic models that rely on prior knowledge about target kinematics, the proposed method learns the kinematic model with a Long Short-Term Memory (LSTM) network. On this basis, the 3D state of fish at each moment is predicted by LSTM network. We propose to use an innovative master-view-tracking-first strategy. The fish are first tracked in the master view. Cross-view association is then established utilizing motion continuity and epipolar constraint cues. Experiments on data sets of different fish densities show that the proposed method is effective and outperforms the state-of-the-art methods.