基于角点光流与SVM的增氧机工作状态检测

在水产养殖中,实时检测增氧机的工作状态非常重要。因此,提出了一种基于角点光流与支持向量机SVM模型的增氧机工作状态检测方法。该方法首先通过摄像机采集增氧机停止/运行状态的视频,然后对相继前后两个视频帧,利用Harris算法检测前一帧图像的水花角点,再根据后一帧,用金字塔Lucas-kanade光流法计算角点的光流量,从而得出水花角点在两帧之间的帧间平均位移;然后,利用学习阶段视频的角点帧间平均位移数据训练SVM模型;最后利用训练好的SVM模型对增氧机实时工作状态进行判断。在工作阶段,采用一种过滤异常视频帧的方法,提高检测的准确率。通过实验表明,该方法适应于对不同光照、不同视频获取角度和不同拍摄距离条件下增氧机工作状态的实时监控,检测准确率高于直方图阈值分割方法。同时,该方法具有较好的鲁棒性和实时性。

Aerator state detection based on corner optical flow and SVM algorithm

Real time detection of the aerator state is an important job in aquiculture. We propose a method to detect the state of the aerator based on corner optical flow and support vector machine (SVM ) model. We firstly collect the videos of stopping and running states of the aerator through camera. Then, we extract two adjacent frames in sequence of the video frames, use the Harris algorithm to detect the interest points of spray in the former frame and calculate its optical flow by the pyramid Lucas kanade algorithm according to the latter frame, thus the average displacement of the interest points between the two frames is obtained. Thirdly, we train the SVM model by the average displacement data of the interest points between the two frames at the learning phase, and utilize the trained SVM model to predict the status of the aerator accordingly at the detecting phase. In addition, we introduce a method to eliminate the frames with abnormal average displacement of the interest points to improve the detection accuracy. Experimental results show that our method is robust and can be adapted to monitor the state of the aerator in real time under conditions such as various illumination, different shooting angles and distances with higher detection accuracy than that of the histogram thresholding method.