基于单类支持向量机的视频车辆检测研究  

Video vehicle detection research based on one-class SVM classification

作　　者：高海迪[1] 詹智财[1] 沈项军[1] 曾兰玲[1] 

机构地区：[1]江苏大学计算机科学与通信工程学院,江苏镇江212013

出　　处：《计算机工程与设计》2013年第5期1735-1739,共5页Computer Engineering and Design

基　　金：国家科技创新基金项目(10C26213200946);国家自然科学基金项目(61005017)

摘　　要：针对现有视频车辆检测方法存在高误检率、不完整及需要分类的样本数量大等问题,提出一种基于单类支持向量机的视频车辆检测方法。方法通过基于视频多帧统计的方法获得完整的车道背景图像,采用单类支持向量机获得车道背景模型,应用此模型检测车辆目标区域。实验结果表明,该算法具有所需样本量小,车辆检测完整,对光照的变化具有一定的自适应能力等特点。The existing video vehicle detection methods have the defections of high false positive rate, incomplete vehicle detection and large number of samples to be classified. A video vehicle detection method based on one-class support vector machine （SVM） is proposed. Firstly, complete lane background images are obtained with video multi-frame statistics approach, and then one-class SVM is utilized to achieve lane background model. Finally, this model is applied to obtain the vehicle target area. The experimental results show that the algorithm has the characteristics of small sample size, complete vehicle detection and self-adaptation ability to illumination conditions.

关 键 词：单类支持向量机 多帧统计 视频车辆检测 车道背景 车辆完整性检测 

分 类 号：TP37[自动化与计算机技术—计算机系统结构]