关键词: 角点检测, 光流法, 图像金字塔, 支持向量机

Abstract:

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.

Key words: corner detection;optical flow method;image pyramid;support vector machine