驾驶员因素引发的交通事故比例居高不下,因此,研究基于驾驶员活动状态分析从而对异常驾驶行为进行正确识别分类的识别方法具有重要意义。提出一种基于协方差流形和基于二分类思想的多类LogitBoost分类器的异常驾驶行为识别方法。首先,提取图像的纹理、颜色和梯度方向等基础特征,以克服基于单一特征识别驾驶行为的不足;然后,利用协方差流形进行多特征融合,以消除特征冗余,同时降低由于不同特征数值差异过大而可能给图像处理及识别带来的影响;最后,使用基于二分类的多类LogitBoost分类器进行分类识别。实验结果表明,相对传统的直接使用LogitBoost的多分类方法,本文方法较大幅地提高了多分类的正确率,针对不同目标的正确识别率可达81.08%。

The proportion of traffic accidents caused by driver factors is high, therefore, it is of great significance to study a recognition method for the correct identification of abnormal driving behavior by analyzing the driver activity state. We propose a recognition method of abnormal driving behavior based on the covariance manifold and two classification of multi-class LogitBoost classifier. First, we extract the basic features, such as texture, color and gradient direction, to overcome the shortage of recognition of driving behavior based on a single feature. Then, we use the covariance manifolds for the multi-feature fusion to eliminate the feature redundancy and reduce the impact of image processing and recognition due to excessive differences in numerical values of different features. Finally, the classification and identification are performed using a multi-class LogitBoost classifier based on two classification. The experimental results show that compared with the traditional multi-class LogitBoost method, the proposed method greatly improves the correct rate of multi-classification, and the correct recognition rate for different targets can reach 81.08%.