计算机科学与探索2018，Vol.12Issue(5)：708-718,11.DOI:10.3778/j.issn.1673-9418.1708030
改进的卷积神经网络在行人检测中的应用
Application of Preprocessing Convolutional Neural Network in Pedestrian Detection
摘要
Abstract
In order to solve the problems of large computational complexity,complicated pedestrian feature extrac-tion and complex background influence,this paper proposes a modified convolutional neural network(CNN)model. Based on the traditional CNN algorithm,a selective attention layer is added to this model to simulate the selective attention feature of human's eyes,which is able to filter the complex background and highlight the characteristics of pedestrians. LBP(local binary pattern) texture processing and gradient processing are used to train the selective attention layer,and the optimal model is obtained by comparing the training results.Experiments are conducted on INRIA, NICTA and Daimler pedestrian datasets respectively. The results show that the accuracy of the proposed model in the pedestrian detection is better than that of the traditional CNN, HOG+SVM, Haar+SVM and PCA+SVM,and the accuracy of the INRIA,NICTA and Daimler pedestrian datasets is 96.14%,96.64% and 99.78% respectively.关键词
行人检测/深度学习/卷积神经网络/选择性注意Key words
pedestrian detection/deep learning/convolutional neural network/selective attention分类
信息技术与安全科学引用本文复制引用
谢林江,季桂树,彭清,罗恩韬..改进的卷积神经网络在行人检测中的应用[J].计算机科学与探索,2018,12(5):708-718,11.基金项目
The National Natural Science Foundation of China under Grant Nos.61632009,61472451,61402161(国家自然科学基金). （国家自然科学基金）
