[1]王海,蔡英凤,陈龙,等.基于Haar-NMF特征和改进SOMPNN的车辆检测算法[J].东南大学学报(自然科学版),2016,46(3):499-504.[doi:10.3969/j.issn.1001-0505.2016.03.008]
 Wang Hai,Cai Yingfeng,Chen Long,et al.Vehicle detection algorithm based on Haar-NMF features and improved SOMPNN[J].Journal of Southeast University (Natural Science Edition),2016,46(3):499-504.[doi:10.3969/j.issn.1001-0505.2016.03.008]
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基于Haar-NMF特征和改进SOMPNN的车辆检测算法
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
46
期数:
2016年第3期
页码:
499-504
栏目:
计算机科学与工程
出版日期:
2016-05-20

文章信息/Info

Title:
Vehicle detection algorithm based on Haar-NMF features and improved SOMPNN
作者:
王海1蔡英凤2陈龙2江浩斌1
1江苏大学汽车与交通工程学院, 镇江212013; 2江苏大学汽车工程研究院, 镇江212013
Author(s):
Wang Hai1 Cai Yingfeng2 Chen Long2 Jiang Haobin1
1School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
2 Automotive Engineering Research Institution, Jiangsu University, Zhenjiang 212013, China
关键词:
车辆工程 车辆检测 Haar特征 非负矩阵分解 改进SOMPNN 高级驾驶辅助系统
Keywords:
automotive engineering vehicle detection Haar feature nonnegative matrix factorization(NMF) improved SOMPNN advanced driver assistant system(ADAS)
分类号:
TP391.4
DOI:
10.3969/j.issn.1001-0505.2016.03.008
摘要:
为解决传统基于Haar特征和自组织映射概率神经网络(SOMPNN)的车辆检测算法中存在当Haar特征向量维数过大时决策时间缓慢和因平滑因子σ单一易导致分类错误的2个不足,提出了一种用低维的Haar-NMF特征代替Haar特征和平滑因子自适应修正的改进SOMPNN(ISOMPNN)车辆检测算法.首先用非负矩阵分解对Haar特征进行降维,生成低维Haar-NMF特征;其次,以SOM输出层神经元的原型向量数作为修正因子,构建了指数函数形式的平滑因子修正函数,并以修正后的平滑因子训练SOMPNN分类器.实验结果表明,与传统的Haar+SOMPNN算法相比,采用Haar-NMF和ISOMPNN构建的车辆检测分类器在检测率、误检率和检测时间等性能指标上都获得明显提升.
Abstract:
The traditional vehicle detection algorithm based on Haar features and self-organized mapping probability neural networks(SOMPNN)has two shortages: High-dimensional Haar features usually cause long decision time; the constant smooth factor σ of SOMPNN often causes false classification. To solve these problems, low-dimensional Haar-NMF(non-negative matrix factorization)features instead of Haar features and an improved SOMPNN(ISOMPNN)with adaptive smooth factor correction are adopted to build the vehicle detector. First, NMF is used to generate low-dimensional Haar-NMF features. Then, the neuron number of the output layer of SOM is set as a correction factor to build the smoothing factor correction function in the form of the exponential function. The SOMPNN classifier is trained with the corrected smoothing factor. Experimental results demonstrate that the performance of the Haar-NMF+ISOMPNN-based vehicle detection classifier is improved in the detection rate, false detection rate and detection time compared with the traditional Haar+SOMPNN-based algorithm.

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备注/Memo

备注/Memo:
收稿日期: 2015-09-06.
作者简介: 王海(1983—),男,博士,副教授,wanghai1019@163.com.
基金项目: 国家自然科学基金资助项目(61573171, 61403172, 51305167)、江苏省自然科学基金资助项目(BK20140555)、中国博士后基金特别资助项目(2015T80511)、中国博士后基金资助项目(2014M561592)、江苏省六大人才高峰资助项目(2014-DZXX-040, 2015-JXQC-012).
引用本文: 王海,蔡英凤,陈龙,等.基于Haar-NMF特征和改进SOMPNN的车辆检测算法[J].东南大学学报(自然科学版),2016,46(3):499-504. DOI:10.3969/j.issn.1001-0505.2016.03.008.
更新日期/Last Update: 2016-05-20