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。
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 handcrafted 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.
作者
李锦明
曲毅
裴禹豪
扆泽江
LI Jinming;QU Yi;PEI Yuhao;YI Zejiang(College of Information Engineerng,Engineering University of PAP,Xi’an 710086,China)
出处
《计算机工程与应用》
CSCD
北大核心
2018年第20期219-222,229,共5页
Computer Engineering and Applications
基金
国家自然科学基金(No.6111238)
关键词
行人重识别
卷积神经网络
预训练模型
深层特征
pedestrian re-identification
convolutional neural network
pre-trained model
deep features
