  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.
