scholarly journals Water Quality Monitoring Method Based on TLD 3D Fish Tracking and XGBoost

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Shuhong Cheng ◽  
Shijun Zhang ◽  
Leihua Li ◽  
Dianfan Zhang
Keyword(s):  
Water Quality ◽  
Tracking System ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Gradient Boosting ◽  
Monitoring Method ◽  
Monitoring Model ◽  
Biological Water ◽  
Extreme Gradient Boosting ◽  
Movement Parameters

Aiming at the problem of water quality monitoring, this paper presents a method of biological water quality monitoring based on TLD (Tracking-Learning-Detection) framework and XGBoost (eXtreme Gradient Boosting). Firstly, under the framework of TLD, an independent tracking system is designed; TLD captures 3D coordinate information of fish based on video and calculates the behavior of fish movement parameters which can reflect the change of water quality via processing the coordinate information of the fish body. The data of coordinate information will be more prominent via the data processing. The integration of all built XGBoost water quality monitoring model which is based on characteristic parameters; the model was used to analyze and evaluate fish behavior parameters under unknown water quality to achieve the purpose of water quality monitoring.

scholarly journals Abnormal Water Quality Monitoring Based on Visual Sensing of Three-Dimensional Motion Behavior of Fish

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1179
Author(s):  
Shuhong Cheng ◽  
Kaopeng Zhao ◽  
Dianfan Zhang
Keyword(s):  
Water Quality ◽  
Three Dimensional ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Support Vector ◽  
Motion Trajectory ◽  
Integrated Learning ◽  
Monitoring Method ◽  
Data Set ◽  
Extreme Gradient Boosting

In the context of the problem of water pollution, the movement characteristics and patterns of fish under normal water quality and abnormal water quality are clearly different. This paper proposes a biological water quality monitoring method combining three-dimensional motion trajectory synthesis and integrated learning. The videos of the fish movement are captured by two cameras, and the Kuhn-Munkres (KM) algorithm is used to match the target points of the fish body. The Kalman filter is used to update the current state and find the optimal tracking position as the tracking result. The Kernelized Correlation Filters (KCF) algorithm compensates the targets that are lost in the tracking process and collision or occlusion in the movement process, reducing the errors caused by illumination, occlusion and water surface fluctuation effectively. This algorithm can directly obtain the target motion trajectory, avoiding the re-extraction from the centroid point in the image sequence, which greatly improves the efficiency. In order to avoid the one-sidedness of the two-dimensional trajectory, the experiment combines the pixel coordinates of different perspectives into three-dimensional trajectory pixel coordinates, so as to provide a more authentic fish swimming trajectory. We then select a representative positive and negative sample data set; the number of data sets should have symmetry. The base classifier capable of identifying different water quality is obtained by training. Finally, support vector machine(SVM), eXtreme Gradient Boosting (XGBoost) and pointnet based classifiers are combined into strong classifiers through integrated learning. The experimental results show that the integrated learning model can reflect the water quality effectively and accurately under the three-dimensional trajectory pixel coordinates of fish, and the recognition rate of water quality is above 95%.

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