Recognition of driving postures by contourlet transform and random forests
Recognition of driving postures by contourlet transform and random forests
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DOI：
10.1049/iet-its.2011.0116
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发表时间：
2012-06
影响因子：
2.7
通讯作者：
Chihang Zhao;Bailing Zhang;Jianhong He;Jie Lian
中科院分区：
文献类型：
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作者：
Chihang Zhao;Bailing Zhang;Jianhong He;Jie Lian
提出了一种基于同态滤波、类肤色区域分割和轮廓波变换的视频姿态特征提取方法。从东南大学（SEU）创建的驾驶姿势数据集中提取特征，使用随机森林（RF）分类器进行驾驶姿势分类的保持和交叉验证实验。与线性感知器分类器、k -最近邻分类器和多层感知器（MLP）分类器等常用分类方法进行了比较，实验结果表明，RF分类器的分类性能最好。在四个预定义的类别中，即，抓住方向盘、操作变速器、吃东西和在蜂窝电话上通话，吃东西的类别是最难分类的。在保持和交叉验证实验中，使用RF分类器对进食的分类准确率超过88%，从而证明了所提出的特征提取方法的有效性以及RF分类器在以人为中心的驾驶辅助系统中自动理解和表征驾驶员行为的重要性。
An efficient feature extraction approach for driving postures from a video camera, which consists of Homomorphic filtering, skin-like regions segmentation and contourlet transform (CT), was proposed. With features extracted from a driving posture dataset created at Southeast University (SEU), holdout and cross-validation experiments on driving posture classification were then conducted using random forests (RF) classifier. Compared with a number of commonly used classification methods including linear perceptron classifier, k -nearest-neighbour classifier and multilayer perceptron (MLP) classifier, the experiments results showed that the RF classifier offers the best classification performance among the four classifiers. Among the four predefined classes, that is, grasping the steering wheel, operating the shift gear, eating and talking on a cellular phone, the class of eating is the most difficult to classify. With RF classifier, the classification accuracies of eating are over 88% in holdout and cross-validation experiments, thus demonstrating the effectiveness of the proposed feature extraction method and the importance of RF classifier in automatically understanding and characterising driver%s behaviours towards human-centric driver assistance systems.