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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帽佩戴检测

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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:在变电站中,安全帽的佩戴检测非常重要。提出了一种基于图像处理和机器学习的新颖实用的安全帽磨损检测方法。首先,利用ViBe背景建模算法在变电站的固定监控摄像机的视线下检测运动物体。获得感兴趣的运动区域后,提取定向梯度直方图(HOG)特征以描述内部人类。然后,基于HOG特征提取的结果,对支持向量机(SVM)进行训练以对行人进行分类。最后,安全头盔的检测将通过颜色特征识别来实现。令人信服的实验结果证明了我们提出的方法的正确性和有效性。

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