Abstract: In order to solve the problems of large computational complexity, complicated pedestrian feature extraction 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.

Key words: pedestrian detection, deep learning, convolutional neural network, selective attention

摘要： 针对当前行人检测方法计算量大，行人特征提取复杂，检测结果易受复杂背景影响等问题，提出一种改进的卷积神经网络（convolutional neural network，CNN）模型。该模型在传统CNN基础上加入选择性注意层，模拟人眼的选择性注意功能，过滤复杂背景，突出行人特征。分别采用LBP（local binary pattern）纹理处理和梯度处理对选择性注意层进行训练，对比训练结果得到最优模型。分别在INRIA、NICTA和Daimler行人数据集上进行实验，结果表明，该模型在行人检测中准确率明显优于传统CNN、HOG+SVM、Haar+SVM、PCA+SVM，在INRIA、NICTA和Daimler行人数据集上的准确率分别达到了96.14%、96.64%和99.78%。

关键词: 行人检测, 深度学习, 卷积神经网络, 选择性注意