A real time expert system for anomaly detection of aerators based on computer vision and surveillance cameras

•Introduce a real-time expert system for anomaly detection of aerators.•Propose a novel RF-KLT algorithm for motion feature extraction in fixed region.•Develop a small object region detection method in complex background.•Present a time series dimension reduction method to build feature dataset.•Object region and working state detection accuracy is 100% and 99.9% respectively. Aerators are essential and crucial auxiliary devices in intensive culture, especially in industrial culture in China. In this paper, we propose a real-time expert system for anomaly detection of aerators based on computer vision technology and existing surveillance cameras. The expert system includes two modules, i.e., object region detection and working state detection. First, we present a small object region detection method based on the region proposal idea. Moreover, we propose a novel algorithm called reference frame Kanade-Lucas-Tomasi (RF-KLT) algorithm for motion feature extraction in fixed regions. Then, we describe a dimension reduction method of time series for establishing a feature dataset with obvious boundaries between classes. Finally, we use machine learning algorithms to build the feature classifier. The proposed expert system can realize real-time, robust and cost-free anomaly detection of aerators in both the actual video dataset and the augmented video dataset. Demo is available at https://youtu.be/xThHRwu_cnI.