Abnormal behavior detection method of fish school under low dissolved oxygen stress based on image processing and compressed sensing - LU Huanda; YU Xin; LIU Guangqiang (浙江大学学报. 农业与生命科学版)
In order to overcome the time-consuming and laborious problems of artificial observation, we proposed an automatic detection method of abnormal behavior of fish school under low dissolved oxygen stress based on image processing and compressed sensing algorithm. Taking Cyprinus carpio as the research object, by obtaining the video images of fish school behaviors under two conditions of normoxia and hypoxia, we used the image processing technology to get the location histogram of fish school, of which the average, variance, skewness, kurtosis and energy were extracted to form the fish movement characteristic parameters of each image. On this basis, the data dictionary matrix was constructed, and the abnormal behavior detection of fish school under low dissolved oxygen stress was implemented by compressed sensing classification. The results showed that the detection method can effectively detect the abnormal behavior of fish school under the low dissolved oxygen stress, with the detection accuracy rate of 98.50%.