A method for detecting miners based on helmets detection in underground coal mine videos

Abstract

In order to monitor dangerous areas in coal mines automatically, we propose to detect helmets from underground coal mine videos for detecting miners. This method can overcome the impact of similarity between the targets and their background. We constructed standard images of helmets, extracted four directional features, modeled the distribution of these features using a Gaussian function and separated local images of frames into helmet and non-helmet classes. Out experimental results show that this method can detect helmets effectively. The detection rate was 83.7%.