3D reconstruction of fish schooling kinematics from underwater video

This paper describes a probabilistic framework to estimate the shape and position of multiple fish in a school. We model the fish shape as an ellipsoid with a curvature coefficient that allows us to incorporate bending. An expression for the extremal contour in terms of state parameters is used to derive a likelihood function for shape. We present a motion model that uses curvature as an input to the turning rate. Tracking is performed using a particle filter with joint probabilistic data association. We evaluate our algorithm using simulated data and further characterize its performance using real data from a laboratory experiment with six giant danios.