东南大学学报（自然科学版）2016，Vol.18Issue(3)：499-504,6.DOI:10.3969/j.issn.1001-0505.2016.03.008
基于 Haar-NMF 特征和改进 SOMPNN 的车辆检测算法
Vehicle detection algorithm based on Haar-NMF features and improved SOMPNN
摘要
Abstract
The traditional vehicle detection algorithm based on Haar features and self-organized map-ping probability neural networks (SOMPNN)has two shortages:High-dimensional Haar features u-sually cause long decision time;the constant smooth factor σof SOMPNN often causes false classifi-cation.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 fac-tor correction are adopted to build the vehicle detector.First,NMF is used to generate low-dimen-sional Haar-NMF features.Then,the neuron number of the output layer of SOM is set as a correc-tion 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 demon-strate that the performance of the Haar-NMF +ISOMPNN-based vehicle detection classifier is im-proved in the detection rate,false detection rate and detection time compared with the traditional Haar +SOMPNN-based algorithm.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Key words
automotive engineering/vehicle detection/Haar feature/nonnegative matrix factoriza-tion(NMF)/improved SOMPNN/advanced driver assistant system(ADAS)分类
信息技术与安全科学引用本文复制引用
王海,蔡英凤,陈龙,江浩斌..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）,2016,18(3):499-504,6.基金项目
国家社会科学基金重点项目“康德实践哲学的义理系统及其道德趋归研究”（14AZX020）阶段性成果。 （）
