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Safety helmet wearing detection based on image processing and machine learning

机译:基于图像处理和机器学习的安全头盔佩戴检测

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Safety helmet wearing detection is very essential in power substation. This paper proposed a innovative and practical safety helmet wearing detection method based on image processing and machine learning. At first, the ViBe background modelling algorithm is exploited to detect motion object under a view of fix surveillant camera in power substation. After obtaining the motion region of interest, the Histogram of Oriented Gradient (HOG) feature is extracted to describe inner human. And then, based on the result of HOG feature extraction, the Support Vector Machine (SVM) is trained to classify pedestrians. Finally, the safety helmet detection will be implemented by color feature recognition. Compelling experimental results demonstrated the correctness and effectiveness of our proposed method.
机译:安全头盔佩戴检测对于功率变电是非常重要的。本文提出了一种基于图像处理和机器学习的创新和实用的安全头盔佩戴方法。首先,利用Vibe背景建模算法在电力变电站中的修复监控摄像机视图下检测运动对象。在获得感兴趣的运动区域之后,提取取向梯度(HOG)特征的直方图以描述内部人。然后,基于HOG特征提取的结果,支持向量机(SVM)培训以对行人进行分类。最后,安全头盔检测将由颜色特征识别实现。令人信服的实验结果表明了我们所提出的方法的正确性和有效性。

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