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

摘要： 针对行人重识别中传统的人工提取的行人浅层特征因受摄像机角度、光照等外界环境的影响，鲁棒性不好，收敛速度慢的问题，研究使用预训练卷积神经网络模型在行人数据库上进行微调的方法，对行人图片进行特征提取，从而得到高维的深层行人特征，最后通过欧氏距离进行相似性的度量。实验结果证明，深层的行人特征在平均准确度评估标准上，相比于传统的人工设计特征，分别得到了9.51%、11.12%、16.63%、16.96%的提高，收敛速度也变得更快，说明深层特征的行人识别能力更强。

关键词: 行人重识别, 卷积神经网络, 预训练模型, 深层特征