计算机应用与软件2017，Vol.34Issue(8)：220-224,256,6.DOI:10.3969/j.issn.1000-386x.2017.08.039
基于改进的CNN多级分类的行人检测算法研究
A PEDESTRIAN DETECTION METHOD BASED ON IMPROVED CNN MULTISTAGE CLASSIFICATION
摘要
Abstract
We present an improved pedestrian detection method for video monitoring application, using support vector machines and convolutional neural networks.First, we located initial suspicious targets of interested area by motion detection, and then calculated the gray level co-occurrence matrix of image patches of these areas.Moreover, the principal component analysis method was used to extract the texture feature vector, and the support vector machine was used to classify the texture and filter out the interference region.Finally, multi-scale image blocks were constructed for the remaining area, the LeNet5 architecture of convolutional neural network was used to execute pedestrian classification.Experimental results on Caltech dataset show that this method has a high true positive rate and low false positive rate.关键词
行人检测/运动检测/灰度共生矩阵/支持向量机/卷积神经网络Key words
Pedestrian detection/Motion detection/Gray level co-occurrence matrix/Support vector machines/Convolutional neural networks分类
信息技术与安全科学引用本文复制引用
杨杰,杨振南..基于改进的CNN多级分类的行人检测算法研究[J].计算机应用与软件,2017,34(8):220-224,256,6.基金项目
湖南省科技计划项目(2014FJ6095) （2014FJ6095）
永州市2016年度科技创新项目(永科发[2016]27号) （永科发[2016]27号）
湖南科技学院计算机应用技术重点学科资助. （）
