[1]伍鹏瑛,张建明,彭建,等.多层卷积特征的真实场景下行人检测研究[J].智能系统学报,2019,14(02):306-315.[doi:10.11992/tis.201710019]
 WU Pengying,ZHANG Jianming,PENG Jian,et al.Research on pedestrian detection based on multi-layer convolution feature in real scene[J].CAAI Transactions on Intelligent Systems,2019,14(02):306-315.[doi:10.11992/tis.201710019]
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多层卷积特征的真实场景下行人检测研究(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年02期
页码:
306-315
栏目:
出版日期:
2019-03-05

文章信息/Info

Title:
Research on pedestrian detection based on multi-layer convolution feature in real scene
作者:
伍鹏瑛12 张建明12 彭建12 陆朝铨12
1. 长沙理工大学 综合交通运输大数据智能处理湖南省重点实验室, 湖南 长沙 410114;
2. 长沙理工大学 计算机与通信工程学院, 湖南 长沙 410114
Author(s):
WU Pengying12 ZHANG Jianming12 PENG Jian12 LU Chaoquan12
1. Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha 410114, China;
2. School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
关键词:
行人检测卷积神经网络SSD真实场景多尺度特征目标检测小目标行人行人数据集
Keywords:
pedestrian detectionCNNsingle shot multibox detectorreal scenemulti-scale featuresobject detectionsmall target pedestriansPedestrian dataset
分类号:
TP391
DOI:
10.11992/tis.201710019
摘要:
针对真实场景下的行人检测方法存在漏检、误检率高,以及小尺寸目标检测精度低等问题,提出了一种基于改进SSD网络的行人检测模型(PDIS)。PDIS通过引出更底层的输出特征图改进了原始SSD网络模型,并采用卷积神经网络不同层输出的抽象特征对行人目标分别做检测,融合多层检测结果,提升了小目标行人的检测性能。此外,针对数据集样本多样性能有效地提升检测算法的泛化能力,本文采集了不同光照、姿态、遮挡等复杂场景下的行人图像,对背景比较复杂的INRIA行人数据集进行了扩充,在扩增的行人数据集上训练的PDIS模型,提高了在真实场景下的行人检测精度。实验表明:PDIS在INRIA测试集上测试结果达到93.8%的准确率,漏检率低至7.4%。
Abstract:
Pedestrian detection methods in real scenes face some problems due to the high miss detection and false detection as well as the low detection accuracy of small size objects. To solve these problems, a pedestrian detection model based on improved SSD (PDIS) is proposed. The PDIS method improves the original SSD network model by extracting the lower-level output feature maps. It employs the abstract features of different convolutional neural network layers to detect pedestrians respectively, and then integrates the detection results of multi layers to increase the pedestrian detection performance for small sizes. Considering that the diversity of dataset can effectively enhance the generalization ability of detection algorithm, the paper expands the INRIA pedestrian dataset with complex background by collecting pedestrian images with different illumination, pose and occlusion. The PDIS method trained on expanded pedestrian dataset increases the precision rate of pedestrian detection in real scenes. The experiment results on INRIA test set indicate that the precision rate of PDIS algorithm is up to 93.8% and the miss rate is as low as 7.4%.

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备注/Memo

备注/Memo:
收稿日期:2017-10-31。
基金项目:国家自然科学基金项目(61402053);湖南省教育厅科研重点项目(16A008);湖南省交通厅科技项目(201446);长沙理工大学研究生科研创新项目(CX2017SS19);长沙理工大学研究生课程建设项目(KC201611).
作者简介:伍鹏瑛,男,1990年生,硕士研究生,主要研究方向为计算机视觉、模式识别。;张建明,男,1976年生,副教授,博士,主要研究方向为计算机视觉、智能交通系统。发表学术论文50余篇,其中EI收录26篇,SCI收录9篇。;彭建,男,1971年生,副教授,主要研究方向为目标检测、计算机视觉。发表学术论文20余篇。
通讯作者:张建明.E-mail:jmzhang@csust.edu.cn
更新日期/Last Update: 2019-04-25