摘要
提出了一种基于支持向量数据描述(SVDD)算法的快速事件检测方法。该算法把有事件样本和无事件样本分别用全体样本优化的SVDD算法进行优化。但每次只对那些对超球体边界有影响的数据进行优化。该方法既保留了全体样本优化SVDD算法的优点,又达到加快训练速度的目的。采用I-880数据库中实际交通的历史数据进行实验,并与全体样本优化SVDD实验结果相比较。实验证明该分类方法能够获得较高检测率和较低的误报率,且需要较短的训练时间,表明了所给方法的有效性和可行性。
The problem of freeway incident detection is researched by using support vector data description （SVDD）. This method optimiza the incident samples and free- flow samples separately, but it only optimizes the dates which have influence to hyper - sphere one time. ＇So it not only holds the advantages of SVDD, and but also gets obtained high＇ training speed. Using the I - 880 database which has actual traffic history data to carry on the experiment, and compares with the classie SVDD, confirmed this classified method can obtain a higher detection rate and lower false alarm rate. So the results of simulation experiments show that the proposed method is effective and feasible.
出处
《计算机技术与发展》
2008年第12期248-250,共3页
Computer Technology and Development
基金
广东省自然科学基金项目(06029813)
广东省高等学校自然科学重点研究项目(05z025)
关键词
SVDD
事件检测
分类
support vector data description
incident detection
classification
