摘要
Abstract
An HOG feature extraction algorithm based on human body is used to study the state monitoring and vision iden-tification technology for the safety management and control system of substation. According to the specific environment of substa-tion,human characteristics and other phenomena,the substation state monitoring and vision identification can be achieved rapid-ly and accurately by means of online classification and offline training of the cascade Adaboost classifier,so as to improve the system technology performance,and make the system practicability stronger. The experimental results show that the detection ac-curacy of the human detection algorithm based on state monitoring and vision identification technology is 93.8%,its false detec-tion rate is 4.7%,and its average consuming time is 62 ms. In comparison with SVM classifier,its detection accuracy is 9.5%higher,the false detection rate is 9.8% lower,and the average consuming time is 132 ms shorter. With the cascade Adaboost classifier,the detection performance can be improved,and the human body region can be extracted in the video sequence quick-ly and accurately,which can meet the requirements of dynamic target detection and analysis.关键词
状态监测/视觉辨识技术/HOG特征提取/Adaboost分类器Key words
state monitoring/vision identification technology/HOG feature extraction/Adaboost classifier分类
信息技术与安全科学
