Research Of Biological Water Quality Warning Method Based On Fish School Tracking Trajectory

Water pollution has always been an important factor affecting human life,health and property safety.However,water quality monitoring is a prerequisite for effective management of water quality.Therefore,how to realize the monitoring and warning of water quality change is an urgent problem to be solved.At present,the common water quality monitoring methods are physicochemical monitoring and biological monitoring.The biological monitoring method can make up for the shortcomings of the physicochemical monitoring method,realize the monitoring of the mixed effect of pollutants,and reflect the water quality changes in a more real-time,accurate and efficient way.Based on the principle of computer vision,this paper takes crucian carps as the indicator of water quality,obtains the video of fish swimming in different environments by adjusting the pH value of water,apply multi-object detection and tracking technology,intelligent information processing and machine learning technology,and studies the biological early warning system based on the tracking trajectory of fish school.The main research contents include:1.The platform of biological water quality warning system and the experiment scheme of fish toxicity were designed.The hardware part of the platform mainly includes three parts: a charge-coupled device camera,experimental fish tank,and a computer.The software system mainly includes five functional modules,such as system introduction,video collection,detection and tracking,record query and water quality warning.Several fish toxicity tests on the platform show that the platform can realize continuous monitoring and water quality warning of fish movement behavior changes.2.This paper studies the detection and tracking method of multiple targets based on Kalman filtering algorithm.Aiming at the problem of detection and tracking difficulty caused by mutual occlusion between individual targets of fish school,a multi-target detection and tracking algorithm based on the motion direction of fish body was proposed.Background difference method is used to detect fish targets.Kalman filtering algorithm was used to estimate the motion state of fish body,and the cost function is established by combining the position of the fish body,target area,and the direction information.The interframe relationship matrix was used to realize the matching correlation of the fish targets in adjacent frames,then the multi-target tracking of fish was completed.The experimental results show that compared with the multi-target tracking algorithm based only on centroid information,the tracking algorithm proposed in this paper has a significant improvement in performance and can effectively deal with the problem of difficult multi-target detection and tracking.3.The quantitative method of characteristic parameters of fish movement behavior is studied.Based on the tracking trajectory of fish school,the individual and group characteristic parameters of the fish are quantified.The individual characteristic parameters include the motion speed and the motion acceleration.the group characteristic parameters include the average swimming speed,average swimming distance and the dispersion of the fish school.The changes of five parameters in the environment of normal water quality(pH = 7)and abnormal water quality(pH = 6,8)were compared.The experimental results showed that there were significant differences in the above fish movement behavior characteristic parameters in different water quality environments,which could provide data support for the biological early warning system.4.The warning method of biological water quality based on machine learning was studied.In order to solve the problems of multiple threshold parameters,complex parameter determination process and low warning accuracy in the existing threshold weighted summation method,a GA-SVM based biological water quality warning algorithm was proposed.The characteristic parameters of fish school motion behavior under normal water quality and abnormal water quality were taken as different sample sets.The SVM model was used to classify the sample sets,and the model parameters were optimized by genetic algorithm to realize the classification of biological water quality warning.The classification accuracy of SVM models with fixed parameters,k-cross validation optimization parameters and GA algorithm optimization parameters was compared.The implementation results show that the model with optimized parameters by GA algorithm has the highest classification accuracy,which verifies the effectiveness of the GA-SVM based biological water quality warning method.