计算机工程与应用 ›› 2011, Vol. 47 ›› Issue (1): 245-248.DOI: 10.3778/j.issn.1002-8331.2011.01.070

• 工程与应用 • 上一篇    

基于主分量分类的交通事件自动检测算法

武林芝,陈淑燕,郑小花   

  1. 南京师范大学 物理科学与技术学院,南京 210097
  • 收稿日期:2009-04-21 修回日期:2009-06-12 出版日期:2011-01-01 发布日期:2011-01-01
  • 通讯作者: 武林芝

Automatic incident detection algorithm based on principal component classifier

WU Linzhi,CHEN Shuyan,ZHENG Xiaohua   

  1. School of Physics Science and Technology,Nanjing Normal University,Nanjing 210097,China
  • Received:2009-04-21 Revised:2009-06-12 Online:2011-01-01 Published:2011-01-01
  • Contact: WU Linzhi

摘要: 利用主分量分类方法,研究改进的基于主分量分类的交通事件自动检测算法。主分量分类方法是一种改进的两类模型分类法。该分类法求解样本方向,该方向可以看作超平面的法方向,根据这个方向将样本中一类数据从另一类数据中分离。样本在法方向上的投影用来估计每个实例的条件概率,然后根据贝叶斯规则实现实例的分类。对于线性不可分等复杂的分类问题,可通过核函数作用将数据映射到高维特征空间中实现线性可分。最后对I-880高速公路事件数据的仿真结果表明,KPCC算法获得了100.00%的检测率、1.82%的误警率和1.02分钟的平均检测时间。

Abstract: An improved automatic incident detection algorithm,based on principal component classifier,is constructed.Principal component classifier is an improved two models classification technology.This method computes a direction from a dataset which can be seen as the normal direction of a hyperlane such that samples in one class can be separated well from the other by this hyperlane.The projections onto that direction can be used for estimating class-conditional possibility density function(pdf) according to Bayes rule.The algorithm can also be carried out in the feature space to deal with more complicated classification problem by mapping input data into a high dimensional feature space using kernel function.Finally the simulation results on I-880 freeway dataset are:DR is 100.00%,FAR is 1.82%,and the MTTD is 1.02 minutes.

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