Experimental Evaluation of Tracking Algorithms Used for the Determination of Fish Behavioral Statistics

The fine-scale swimming behavior of fish can now be studied because of the development of sophisticated measurement devices such as multibeam sonar and stereo video systems. However, even with these sensors, improved methods are still required to generate quality estimates of swimming speeds and turn rates. Biologists have commonly relied on pointwise differentiation of noisy position measurements while engineers have focused on Bayesian algorithms to track underwater vehicles. A comparative evaluation of the performance of these tracking algorithms for the analysis of fine-scale behavior of fish was performed using a data set of 100 fish tracks recorded simultaneously with a multibeam sonar and a stereo video camera system. The segmenting track identifier, a non-Bayesian curve fitting and segmenting tracker, is shown to be most effective for tracking the unpredictable and complex horizontal motion of fish while a Kalman smoother using a constant-velocity model is shown to be most effective for tracking the more predictable and piecewise linear vertical motion of fish. Both are shown to be more effective than pointwise differentiation. Criteria for selecting an appropriate algorithm for a given motion study are provided