Aiming at accurate counting of a number or batch of fish in process of feeding

transporting and selling

a fish counting algorithm based on machine vision tracking is proposed. According to a certain time interval

dynamic track and shoot fish images

use image processing technology to extract area of each image block as its eigenvalue. According to the size of area

the image block is divided into normal image and abnormal image

and the normal image and abnormal image are respectively analyzed and processed. Correlated algorithm model between the image eigenvalue and the number of fish is established. The optimal model parameters are obtained by regression of multiple sets of image data captured by dynamic tracking

determine the estimation function and applied to the count summation. Experimental results show that this method of fry counting based on dynamic visual tracking and regression analysis can effectively distinguish the adhesion problems during fry counting and improve precision of fry counting.