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

TL;DR

This paper presents a real-time computer vision-based expert system utilizing existing surveillance cameras for accurate anomaly detection of aerators, addressing challenges like illumination and occlusion with novel algorithms and machine learning.

Contribution

It introduces a new anomaly detection system combining object region detection, RF-KLT motion feature extraction, and feature classification, improving accuracy and speed over traditional methods.

Findings

Detection accuracy for aerator regions is 100%

Working state detection accuracy is 99.9%

Detection speed ranges from 77 to 333 FPS

Abstract

Aerators are essential and crucial auxiliary devices in intensive culture, especially in industrial culture in China. The traditional methods cannot accurately detect abnormal condition of aerators in time. Surveillance cameras are widely used as visual perception modules of the Internet of Things, and then using these widely existing surveillance cameras to realize real-time anomaly 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 technical and applied challenges, e.g., illumination, occlusion, complex background, etc. To tackle these aforementioned challenges, we propose a real-time expert system based on computer vision technology and existing surveillance cameras for anomaly detection of aerators, which consists of two modules, i.e., object region detection and working state detection. First, it is difficult to detect the working state for some small object regions in whole images, and the time complexity of global feature comparison is also high, so we present an 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 present 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 experimental results in both the actual video dataset and the augmented video dataset show that the accuracy for detecting object region and working state of aerators is 100% and 99.9% respectively, and the detection speed is 77-333 frames per second (FPS) according to the different types of surveillance cameras.