基于改进的Adaboost算法的交通事件自动检测

针对交通领域中的事件检测(无事件模式和事件模式)模式识别问题,提出了一种基于改进的Adaboost算法的交通事件检测方法。阐述了Kohonen神经网络的结构与训练算法,分析了事件对交通流的影响规律,并合理地选取了Kohonen神经网络的输入量;最后采用改进的Adaboost算法对分类结果进行加权投票。仿真实验表明,提出的方法学习速度快、泛化能力好,对交通事件具有较好的检测效果。

Traffic incident detection based on improved Adaboost method

Directing at the problem of mode identification of incident detection in transportation field,a novel method is proposed for traffic incidents detection based on improved adaboost method.The structure and training algorithm of Kohonen neural network are formulated.Then the influence of an incident on the traffic flow is analyzed,and Kohonen neural network input variables are selected reasonably.At last Adaboost algorithm is used to build an integration-neural network.Finally we simulate with Matlab and find out the algorithm has such advantages as fast learning speed,good generalization ability and high detection rate.