Detection and tracking of safety helmet in factory environment

Abstract

Safety helmet plays a vital role in protecting worker’s head in dangerous working environment. During the inspection of safety helmets by means of video automatic monitoring, due to the complex factory environment and large area, the safety helmets worn by workers in the foreground are not easy to be detected, resulting in the problem of safety helmet leakage. To solve this problem, a helmet detection method combining multi-feature fusion and support vector machine (SVM) is proposed to improve the helmet recognition rate. First of all, the visual background difference algorithm was used to detect workers, and the initial positioning of the helmet was determined by the proportional relationship between the head and the whole body. Secondly, this paper uses the principal component analysis method (PCA algorithm) to reduce the dimensionality of the feature vector, cascades the two feature vectors after the dimensionality reduction with the center of gravity, and use the SVM model based on Bayesian optimization to identify the helmet. Finally, a method combining and Meanshift algorithm of multi-feature fusion and Kalman filter is proposed to track the detected helmet. The average recognition rate of multiple experiments is 90.03%. So the helmet tracking algorithm combined with Kalman filter and Meanshift of multi-feature fusion improves the helmet tracking accuracy.