基于小波模极大值和SVM的智能车辆障碍物检测

针对复杂交通场景中智能车辆前向障碍物检测问题,根据障碍物的后视视觉特征,提出了一种基于小波模极大值和支持向量机的障碍物检测方法.利用小波变换对奇异信号的多尺度分析,并结合障碍物先验知识的多特征组合,对候选障碍物区域进行检测;构建了一种适合于交通场景中障碍物分类的二叉树支持向量机(BT-SVM)多类分类器,对候选障碍物区域进行确认识别.将该方法应用于高速公路、城区道路等多种交通场景中,实车实验结果表明了本方法的有效性、实时性和通用性.

Obstacles Detection for Intelligent Vehicle based on WTMM and SVM Classifier

To detect forward obstacles for intelligent vehicle in complex traffic scenes,a novel obstacle detection method based on wavelet transform module maximum(WTMM) and support vector machine(SVM) was presented,considering obvious rear visual features of forward obstacles.Firstly,the candidate regions of obstacle(ROIs) were detected based on multi-scale singularity analysis with WTMM and multi-features combination of obstacle knowledge.Then,these ROIs were recognised based on a compatible binary tree support vector machine(BT-SVM) classifier for obstacle pattern of traffic scenes.The application of the proposed method to different traffic scenes(e.g.,simply structured highway,complex urban street) showed that it has the efficient,real-time and universale ability.