Tuna Nutriment Tracking using Trajectory Mapping in Application to Aquaculture Fish Tank
Hilmil Pradana, Keiichi Horio

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…
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