摘要
随着深度学习方法在行人检测领域的深入应用,基于卷积神经网络的行人检测技术在特征学习、目标分类、边框回归等方面表现出的优势已愈发突出。因此,本文从对传统的行人检测方法和基于卷积神经网络的行人检测技术进行优劣比较切入,概述了卷积神经网络的基础构架,以此引出对当前常用的基于卷积神经网络的常见行人检测技术及其优缺点,最后讨论了现有基于卷积神经网络算法实现行人检测存在的不足和指出今后发展方向。
With the in-depth application of deep learning in pedestrian detection, the advantages of pedestrian detection based on convolutional neural network have become more pronounced in the fields of feature learning, object classification, border regression and others. An overview of basic structure of convolutional neural network is done by comparing the advantages and disadvantages of the pedestrian detection based on the traditional method and convolutional neural network. On this basis, the paper introduces the common pedestrian detection technologies based on convolutional neural network and its advantages and disadvantages. At last, the present deficiencies existing in pedestrian detection based on CNN are briefly discussed and the future directions are pointed out.
作者
谭玉枚
余长庚
TAN Yu-mei;YU Chang-geng(College of Information and Communication Engineering,Hezhou University,hezhou 542899,China)
出处
《软件》
2020年第7期31-36,75,共7页
Software
基金
贺州学院校级科研项目(项目编号:2017ZZZK06)“智能视频中人体的可靠检测与跟踪算法研究”研究成果。
关键词
卷积神经网络
行人检测
目标分类
边框回归
Convolution neural network
Pedestrian detection
Target classification
Border regression
