卜庆志,裘君,胡超.基于 HOG 特征提取与 SVM 驾驶员注意力分散行为检测方法研究[J].集成技术,2019,8(4):69-75
基于 HOG 特征提取与 SVM 驾驶员注意力分散行为检测方法研究
Research on Driver’s Distracted Behavior Detection MethodBased on Histogram of Oriented Gradient Feature Extraction andSupport Vector Machine
  
DOI:10.12146/j.issn.2095-3135.20190527001
中文关键词:  驾驶员注意力分散;方向梯度直方图;交叉验证;支持向量机
英文关键词:driver distraction; histogram of oriented gradient; cross validation; support vector machine
基金项目:宁波市科技计划项目(创新团队 2014B82015);浙江省教育厅一般项目(Y201738805)
作者单位
卜庆志 江西理工大学 赣州 341000;浙江大学宁波理工学院 宁波 315100 
裘君 浙江大学宁波理工学院 宁波 315100 
胡超 浙江大学宁波理工学院 宁波 315100 
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中文摘要:
      驾驶员注意力分散是导致交通事故的主要原因,该文以驾驶员注意力分散行为图像为分类 目标,提出了一种基于方向梯度直方图(Histogram of Oriented Gradient,HOG)与支持向量机(Support Vector Machine,SVM)的行为检测方法。首先,获取图像中的感兴趣区域,并对图像进行增强、去噪及归一化处理;然后,提取图像 HOG 特征,进而采用交叉验证法对 SVM 分类器中的参数进行优化;最后,对视频图像中驾驶员的不同行为进行分类识别。实验中,通过与传统 SVM 算法以及基于局部二值模式的 SVM 算法进行对比,验证了所提方法具有更好的识别准确率。
英文摘要:
      To reduce the occurrence of traffic accidents caused by driver distraction, a behavior detection method based on histogram of oriented gradient (HOG) and support vector machine (SVM) was proposed in this paper. In the algorithm, interesting region of driver was detected first from the video images. Then the image was enhanced, smoothed and normalized. The histogram of oriented gradient was used to extract the feature of the target image. The cross-validation method was used to optimize the SVM parameters, and then used for the classification of driver behaviors. In the experiments, the proposed method was compared with classical SVM and the local binary patterns feature based SVM algorithms. The results show that, the proposed method can obtain better classification accuracy.
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