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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...
3D motion data of fish school is more valuable than 2D data for behavior and other researches. This paper proposes to use a master view tracking first strategy based on a novel master-slave camera setup. On this basis, fish are firstly tracked in master view in 2D after being extracted via an eye-focused Gaussian Mixture Model (E-GMM) detector. Then 3D trajectories are reconstructed by associating...
This paper addresses the problem of 3D tracking deformable surfaces undergoing non-rigid motion from multi-view video sequences. We propose a method that starts from a set of feature points which have been matched across views and time. A data-driven motion propagation technique makes the motion dense enough to give initial guess to the parameter estimation of the vertices of the 3D model. Finally...
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