On-road vehicle verification based on VS-HOG and ELM  

基于垂直对称HOG和极限学习机的在线车辆验证方法(英文)

作　　者：范延军[1,2] 张雷[1] 张为公[1] 

机构地区：[1]东南大学仪器科学与工程学院,南京210096 [2]中国计量学院计算机科学与技术系,杭州310018

出　　处：《Journal of Southeast University(English Edition)》2015年第1期67-73,共7页东南大学学报（英文版）

基　　金：The National Natural Science Foundation of China(No.61203237);the Natural Science Foundation of Zhejiang Province(No.LQ12F03016);the China Postdoctoral Science Foundation(No.2011M500836)

摘　　要：A solution is proposed for the real-time vehicle verification which is an important problem for numerous on- road vehicle applications. First, based on the vertical symmetry characteristics of vehicle images, a vertical symmetrical histograms of oriented gradients （VS-HOG） descriptor is proposed for extracting the image features. In the classification stage, an extreme learning machine （ELM） is used to improve the real-time performance. Experimental data demonstrate that, compared with other classical methods, the vehicle verification algorithm based on VS-HOG and ELM achieves a better trade-off between cost and performance. The computational cost is reduced by using the algorithm, while keeping the performance loss as low as possible. Furthermore, experimental results further show that the proposed vehicle verification method is suitable for on-road vehicle applications due to its better performance both in efficiency and accuracy.为了满足车辆在线应用所需的实时性和准确性,提出了一种在线实时车辆验证的解决方法.首先,基于对车辆图像对称特性的分析,提出了垂直对称HOG描述子,用来提取图像的特征.在车辆分类阶段,为了提高算法的实时性,使用极限学习机作为分类器.与其他经典算法的实验数据进行比较,结果表明基于垂直对称HOG和极限学习机的车辆验证方法能够在算法的运行效果与计算代价方面取得较好的折中,并且能够在尽可能保证算法效果的同时降低计算开销.实验结果进一步表明,提出的车辆验证方法在执行效率和准确性方面均能取得较好的效果,能够满足车辆的在线实时应用要求.

关 键 词：histogram of oriented gradients （HOG） vertical symmetrical histogram of oriented gradients （VS-HOG） vehicle verification extreme learning machine （ELM） 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]