摘要
为了提高交通视频事件检测的准确性和检测速率,提出了一种基于遗传算法的交通视频多特征选择方法.该方法首先提取交通视频的多种特征,尽可能多地获取各种视频事件的信息,然后将这些特征进行融合,得到一个可以表征事件的高维冗余的特征向量,再采用遗传算法对多特征进行优化筛选,最后使用SVM分类器进行训练获得低维的最优特征子集并应用于交通事件检测.实验结果表明,该方法在降低提取特征的维数的同时,可有效提高交通视频事件检测的准确率和检测速率.
A new multi-features selection method on traffic videos which is based on genetic algorithm is proposed to improve the accuracy and rate of the traffic video event detection.This method firstly extracted multiple features of traffic videos to gain information of a variety of traffic events as much as possible,and then fused to those features to get a high-dimensional redundancy feature vector that can characterize the video,then used genetic algorithm to optimize and select multiple feathers and finally obtain the optimal feature set by SVM classifier to detect and analysis traffic events.Experimental results show that this method can effectively reduce the dimension of features and improve the accuracy and rate of the traffic events detection.
出处
《微电子学与计算机》
CSCD
北大核心
2013年第7期42-46,共5页
Microelectronics & Computer
基金
国家自然科学基金项目(61170126)
关键词
交通事件检测
特征融合
多特征选择
遗传算法
traffic events detection
feature fusion
multi-features selection
Genetic Algorithm
