关键词: 行人重识别, 卷积神经网络, 预训练模型, 深层特征

Abstract: In order to solve the problem of the low robustness and slow convergence in the way of hand-crafted features extraction due to the influence of the different camera angle and distinct illumination, it uses the pedestrian database to adjust the pre-trained convolutional neural network model, and then calculates the similarity degree by Euclidean distance. Experimental results show that the deep pedestrian features are 9.51%, 11.12%, 16.63%, 16.96% better than the hand-crafted features in the mean Average Precision（mAP） and the speed of convergence is faster, it indicates that the deep feature can improve the performance of the pedestrian re-identification.

Key words: pedestrian re-identification, convolutional neural network, pre-trained model, deep features