摘要
针对在实际场景中存在的不同行人图像之间比相同行人图像之间更相似所造成的行人再识别准确率较低的问题,提出一种基于孪生网络并结合识别损失和双向最大边界排序损失的行人再识别方法。首先,对在超大数据集上预训练过的神经网络模型进行结构改造,主要是对最后的全连接层进行改造,使模型可以在行人再识别数据集上进行识别判断;其次,联合识别损失和排序损失监督网络在训练集上的训练,并通过正样本对的相似度值减去负样本对的相似度值大于预定阈值这一判定条件,来使得负例图像对之间的距离大于正例图像对之间的距离;最后,使用训练好的神经网络模型在测试集上测试,提取特征并比对特征之间的余弦相似度。在公开数据集Market-1501、CUHK03和DukeMTMC-reID上进行的实验结果表明,所提方法分别取得了89.4%、86.7%、77.2%的rank-1识别率,高于其他典型的行人再识别方法,并且该方法在基准网络结构下最高达到了10.04%的rank-1识别率提升。
Focusing on the low accuracy of person re-identification caused by that the similarity between different pedestrians’images is more than that between the same pedestrians’images in reality,a person re-identification method based on Siamese network combined with identification loss and bidirectional max margin ranking loss was proposed.Firstly,a neural network model which was pre-trained on a huge dataset,especially its final full-connected layer was structurally modified so that it can output correct results on the person re-identification dataset.Secondly,training of the network on the training set was supervised by the combination of identification loss and ranking loss.And according to that the difference between the similarity of the positive and negative sample pairs is greater than the predetermined value,the distance between negative sample pair was made to be larger than that of positive sample pair.Finally,a trained neural network model was used to test on the test set,extracting features and comparing the cosine similarity between the features.Experimental result on the open datasets Market-1501,CUHK03 and DukeMTMC-reID show that rank-1 recognition rates of the proposed method reach 89.4%,86.7%,and 77.2% respectively,which are higher than those of other classical methods.Moreover,the proposed method can achieve a rank-1 rate improvement of up to 10.04% under baseline network structure.
作者
祁子梁
曲寒冰
赵传虎
董良
李博昭
王长生
QI Ziliang;QU Hanbing;ZHAO Chuanhu;DONG Liang;LI Bozhao;WANG changsheng(School of Artificial Intelligence,Hebei University of Technology,Tianjin 300401,China;Beijing Institute of New Technology Applications,Beijing Academy of Science and Technology,Beijing 100035,China)
出处
《计算机应用》
CSCD
北大核心
2019年第4期977-983,共7页
journal of Computer Applications
基金
国家重点研发计划项目(2018YFC08097000
2018YFC0704800
2018YFF0301000)
国家自然科学基金资助项目(91746207)
北京市科学技术研究院萌芽计划项目(GS201817)~~
关键词
行人再识别
孪生网络
双向最大边界
排序损失
卷积神经网络
person re-identification
Siamese network
bidirectional max margin
ranking loss
Convolutional Neural Network(CNN)
