基于卷积神经网络的行人人头检测方法对比研究

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英文篇名：Contrastive Study of the Pedestrian Head Detection Method Based on Convolutional Neural Network 作者：邢志祥 ; 顾凰琳 ; 魏振刚 ; 钱辉 ; 张莹 ; 汪李金 英文作者：XING Zhixiang;GU Huanglin;WEI Zhengang;QIAN Hui;ZHANG Ying;WANG Lijin;School of Environmental & Safety Engineering,Changzhou University; 关键词：行人人头检测方法 ; 卷积神经网络 ; 特征提取 ; 目标检测结构 英文关键词：pedestrian head detection method;;convolutional neural network;;feature extraction;;target detection structure 中文刊名：安全与环境工程 英文刊名：Safety and Environmental Engineering 机构：常州大学环境与安全工程学院; 出版日期：2019-01-30 17:24 出版单位：安全与环境工程 年：2019 期：01 基金：国家自然科学基金项目(51574046);; 江苏省研究生科研与实践创新计划项目(KYCX17_2078) 语种：中文; 页：81-86 页数：6 CN：42-1638/X ISSN：1671-1556 分类号：TP391.41;TP183

摘要

为提高车站客流统计的精度使其可以准确预警,针对传统的客流统计方法步骤繁琐、准确率低等局限性,对基于卷积神经网络的行人人头检测方法进行研究。首先在常州某车站安检站台处通过高位摄像头采集行人的人头数据库;然后通过不同的行人特征提取网络(Inception模块、Resnet、Mobilenet)与Faster R-CNN、SSD、R-FCN等目标检测结构组合的方式来对比探究各种行人人头检测组合模型的准确率和检测速度,并选择最优的行人人头检测方法;最后通过模型试验分析,结果显示Inception-V2特征提取网络与Faster R-CNN目标检测结构组合的行人人头检测模型具有较高的准确率和较优的实时性,这种行人人头检测方法对客流预警具有重要的意义。
        In order to improve the precision of station passenger flow statistics for accurate early-warning,this paper studies the pedestrian head detection method based on convolutional neural network which works without the limitations of traditional passenger flow statistics methods such as cumbersome procedures and low accuracy.Firstly,the paper uses a high-level camera to collect the database of pedestrian heads at a station security checkpoint in Changzhou.Then,the paper combines the feature extractors(Inception module,Resnet,Mobilenet)with the target detection networks(Faster R-CNN,SSD,and R-FCN).Finally,the paper compares the accuracy and speed of them,and chooses the optimal pedestrian head detection method.The experimental results show that the detection accuracy and instantaneity of the pedestrian detection model which combines the Inception-V2 feature extractors with Faster R-CNN networks is better.The pedestrian-headed detection method is of great significance to the passenger flow warning.

引文

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