Fish Motion Estimation Using ML-based Relative Depth Estimation and Multi-Object Tracking

Abstract

Fish motion is a very important indicator of various health conditions of fish swarms in the fish farming industry. Many researchers have successfully analyzed fish motion information with the help of special sensors or computer vision, but their research results were either limited to few robotic fishes for ground-truth reasons or restricted to 2D space. Therefore, there is still a lack of methods that can accurately estimate the motion of a real fish swarm in 3D space. Here we present our Fish Motion Estimation (FME) algorithm that uses multi-object tracking, monocular depth estimation, and our novel post-processing approach to estimate fish motion in the world coordinate system. Our results show that the estimated fish motion approximates the ground truth very well and the achieved accuracy of 81.0% is sufficient for the use case of fish monitoring in fish farms.