关键词: 行人检测, 深度学习, 卷积神经网络, 选择性注意

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