中国计量大学学报2017，Vol.28Issue(4)：472-477,6.DOI:10.3969/j.issn.2096-2835.2017.04.011
卷积神经网络的多尺度行人检测
Multi-scale pedestrian detection based on convolutional neural networks
摘要
Abstract
Pedestrian detection has been widely applied in intelligent surveillance,automatic driving,driver assistance systems,intelligent robots and so on.The traditional pedestrian detection method,by using the moving window traveling over images,leads to heavy computational cost and low speed of pedestrian detection.At present,the pedestrian detection method based on deep learning has entered into a stage of rapid development.But there are still many problems,such as the missing detection of pedestrians in small size.In this paper,we proposed a multi-scale pedestrian detection method based on convolutional neural networks.We analyzed some factors such as the increase of detection layers,parallel convolution layers,changes of the size of convolution kernels,and their impact on the performance of pedestrian detection.The experimental results show that the proposed method can perform well on KITTI data sets.关键词
卷积神经网络/多尺度行人检测/增加检测层/并联卷积层Key words
convolutional neural networks/multi-scale pedestrian detection/increase of detection layer/parallel convolution layer分类
信息技术与安全科学引用本文复制引用
胡葵,章东平,杨力..卷积神经网络的多尺度行人检测[J].中国计量大学学报,2017,28(4):472-477,6.基金项目
浙江省自然科学基金资助项目(No.LY15F020021),浙江省科技厅公益性项目(No.2016C31079). （No.LY15F020021）
