  Controlling fish feeding machine is challenging problem because experienced
fishermen can adequately control based on assumption. To build robust method
for reasonable application, we propose automatic controlling fish feeding
machine based on computer vision using combination of counting nutriments and
estimating ripple behavior using regression and textural feature, respectively.
To count number of nutriments, we apply object detection and tracking methods
to acknowledge the nutriments moving to sea surface. Recently, object tracking
is active research and challenging problem in computer vision. Unfortunately,
the robust tracking method for multiple small objects with dense and complex
relationships is unsolved problem in aquaculture field with more appearance
creatures. Based on the number of nutriments and ripple behavior, we can
control fish feeding machine which consistently performs well in real
environment. Proposed method presents the agreement for automatic controlling
fish feeding by the activation graphs and textural feature of ripple behavior.
Our tracking method can precisely track the nutriments in next frame comparing
with other methods. Based on computational time, proposed method reaches 3.86
fps while other methods spend lower than 1.93 fps. Quantitative evaluation can
promise that proposed method is valuable for aquaculture fish farm with widely
applied to real environment.
