Vision-based 3D Tracking System for Fish Interaction Analysis

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This paper proposes a method to track 3D motion data of a fish group in a fish tank using multiple cameras outside of the tank. As fish are almost identical, occlusion among individuals and reflection at the fish-tank surface make it difficult to maintain the object association during the tracking. To tackle this difficulty, we combine a 3D fish shape model consisting of a flexible midline and fixed elliptical cross sections with the mixture particle filter to exploit both shape and motion priors. During the calculation of the likelihood function, we utilize a pixel-wise varifocal camera model to achieve an efficient forward projection from underwater 3D object to each of image planes considering the effect of light refraction.

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