Automatic Detection of Operator without Safety Helmet

It is very important to protect the safety of the human head with helmet. Traditional detection for helmet wearing mainly relies on manual approach, which was more subjective that a missing condition may happen caused by fatigue and other factors. Owing to this situation, this paper proposed a method for automatic detection of operator without helmet in real-time. Firstly, Gaussian model for background subtraction is used to detect moving target. Secondly, HOG feature extraction can be used to classify the human target from vehicle. Then, a color feature extraction algorithm is proposed for helmet recognition. The algorithm has been applied into the real time monitoring system and verified with higher accuracy.