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Aerators are essential and crucial auxiliary devices in intensive culture,especially in industrial culture in China. The traditional methods cannotaccurately detect abnormal condition of aerators in time. Surveillance camerasare widely used as visual perception modules of the Internet of Things, andthen using these widely existing surveillance cameras to realize real-timeanomaly detection of aerators is a cost-free and easy-to-promote method.However, it is difficult to develop such an expert system due to some technicaland applied challenges, e.g., illumination, occlusion, complex background, etc.To tackle these aforementioned challenges, we propose a real-time expert systembased on computer vision technology and existing surveillance cameras foranomaly detection of aerators, which consists of two modules, i.e., objectregion detection and working state detection. First, it is difficult to detectthe working state for some small object regions in whole images, and the timecomplexity of global feature comparison is also high, so we present an objectregion detection method based on the region proposal idea. Moreover, we proposea novel algorithm called reference frame Kanade-Lucas-Tomasi (RF-KLT) algorithmfor motion feature extraction in fixed regions. Then, we present a dimensionreduction method of time series for establishing a feature dataset with obviousboundaries between classes. Finally, we use machine learning algorithms tobuild the feature classifier. The experimental results in both the actual videodataset and the augmented video dataset show that the accuracy for detectingobject region and working state of aerators is 100% and 99.9% respectively, andthe detection speed is 77-333 frames per second (FPS) according to thedifferent types of surveillance cameras.

Real time expert system for anomaly detection of aerators based on computer vision technology and existing surveillance cameras