Automatic controlling fish feeding machine using feature extraction of nutriment and ripple behavior

Controlling fish feeding machine is a 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 acknowledging the nutriments moving to sea surface. Recently, object tracking is an active research and challenging problem in computer vision. Unfortunately, the robust tracking method for multiple small objects with dense and complex relationships is an 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. The 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 compared with other methods. Based on computational time, the proposed method reaches 3.86 fps while other methods spend lower than 1.93 fps. Quantitative evaluation can promise that the proposed method is valuable for aquaculture fish farm with wide application to real environment.