Tuna Nutriment Tracking using Trajectory Mapping in Application to Aquaculture Fish Tank

TL;DR

This paper presents a novel tracking algorithm for monitoring nutriments in aquaculture tanks, aiding in cost-effective fish feeding management by analyzing fish behavior through video data.

Contribution

Develops a robust nutriment tracking method based on minimum cost problem tailored for complex aquatic environments, improving accuracy and applicability in aquaculture.

Findings

Average error distance of 21.32 pixels

Standard deviation of 3.08 pixels

Method is effective for real aquaculture datasets

Abstract

The cost of fish feeding is usually around 40 percent of total production cost. Estimating a state of fishes in a tank and adjusting an amount of nutriments play an important role to manage cost of fish feeding system. Our approach is based on tracking nutriments on videos collected from an active aquaculture fish farm. Tracking approach is applied to acknowledge movement of nutriment to understand more about the fish behavior. Recently, there has been increasing number of researchers focused on developing tracking algorithms to generate more accurate and faster determination of object. Unfortunately, recent studies have shown that efficient and robust tracking of multiple objects with complex relations remain unsolved. Hence, focusing to develop tracking algorithm in aquaculture is more challenging because tracked object has a lot of aquatic variant creatures. By following aforementioned problem, we develop tuna nutriment tracking based on the classical minimum cost problem which consistently performs well in real environment datasets. In evaluation, the proposed method achieved 21.32 pixels and 3.08 pixels for average error distance and standard deviation, respectively. Quantitative evaluation based on the data generated by human annotators shows that the proposed method is valuable for aquaculture fish farm and can be widely applied to real environment datasets.