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Fatigue Detection Based on Regional Local Binary Patterns Histogram and Support Vector Machine

机译:基于区域局部二进制模式直方图和支持向量机的疲劳检测

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Driver fatigue detection is a challenging problem in intelligent transportation system. The detection accuracy suffered by different illumination. This paper proposed a fatigue detection method based on regional local binary patterns histogram (RLBPH) and support vector machine (SVM). Firstly, we division the face image into blocks, then using local binary patterns(LBP) operator to present each block and calculating the LBP histogram(LBPH) of each block, then combine them into a RLBPH to present the face image. We used the fatigue face sample RLBPH feature and normal face sample RLBPH feature to train the SVM to get its model and the parameters. We input the RLBPH feature of the testing sample to the trained models, thus can classify the RLBPH feature of the testing sample. In our experiments we observe that RLBPH features perform stably and robustly on different illumination, and yield promising performance in low-resolution images captured from web cam.
机译:驾驶员疲劳检测是智能交通系统的一个具有挑战性的问题。 通过不同照明遭受的检测精度。 本文提出了一种基于区域局部二进制图案直方图(RLBPH)和支持向量机(SVM)的疲劳检测方法。 首先,将面部图像划分为块,然后使用本地二进制模式(LBP)操作员呈现每个块并计算每个块的LBP直方图(LBPH),然后将它们组合成RLBPH以呈现面部图像。 我们使用了疲劳面样本RLBPH功能和普通面部样本RLBPH功能,培训SVM以获得其模型和参数。 我们将测试样本的RLBPH功能输入到培训的型号,从而可以对测试样本的RLBPH功能进行分类。 在我们的实验中,我们观察到RBPH特征在不同的照明中稳定且稳健地进行,并在从卷筒纸凸轮捕获的低分辨率图像中产生有希望的性能。

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