Working clothes detection of substation workers based on the image processing

Abstract

On account of the substation is a basis and important element of the power system, its maintenance plays a pivotal role in the stable operation of power grid. As the maintainer of the substation, the on-site staffs work long-term in strong electromagnetic field environment. Therefore, it is necessary to wear the working clothes strictly. In order to strengthen the working clothes wearing circumstance supervision, its better to carry out the real-time supervision on the on-site staffs. In this paper, a video-based working clothes wearing circumstance detection method was put forward. Firstly, we extract characteristics by HOG(Histogram of Oriented Gradient) method and the color spatial distribution compactness presented in this paper. Secondly, the SVM(Support Vector Machine) classifier is trained to realize the substation maintainer detection. Finally, we model the electricity working clothes in the HSV(Hue, Saturation, Value) color space and combine the performance characteristics to get the final results. The experimental results demonstrate that this method has a high accuracy in the substation surveillance video.