基于新型三元卷积神经网络的行人再辨识算法

基于三元卷积神经网络的行人再辨识算法多数采用欧式距离度量行人之间的相似度，并配合铰链(hinge)损失函数进行卷积神经网络的训练。然而，这种作法存在两个不足:欧式距离作为行人相似度，鉴别力不够强；铰链损失函数的间隔(Margin)参数设定依赖于人工预先设定且在训练过程中无法自适应调整。为此，针对上述两个不足进行改进，该文提出一种基于新型三元卷积神经网络的行人再辨识算法，以提高行人再辨识的准确率。首先，提出一种归一化混合度量函数取代传统的度量方法进行行人相似度计算，提高了行人相似度度量的鉴别力；其次，提出采用Log-logistic函数代替铰链函数，无需人工设定间隔参数，改进了特征与度量函数的联合优化效果。实验结果表明，所提出的算法在Auto Detected CUHK03 和VIPeR两个数据库上的准确率均获得显著的提升，验证了所提出算法的优越性。

Person Re-identification Based on Novel Triplet Convolutional Neural Network

Most triplet Convolutional Neural Network (CNN) based person re-identification algorithms use the Euclidean distance as the similarity measurement between a pair of person images, and utilize the hinge loss function to train CNNs. However, there are two disadvantages in these approaches: the Euclidean distance is not discriminative enough for measuring person similarities; the margin parameter of the hinge loss function must be manually set in advance and it can not be adaptively adjusted. For these, a novel triplet convolutional neural network based person re-identification algorithm is proposed to solve the above two disadvantages for improving the accuracy. First, the normalization hybrid similarity function is proposed to replace Euclidean distance to obtain a more discriminative person similarity measurement. Second, the Log-logistic function is designed to replace the hinge function, which does not need to set the margin parameter so that the joint optimization effect of feature learning and similarity learning is improved. The experimental results on the Auto Detected CUHK03 and VIPeR databases show that the proposed method gains significant improvements in person re-identification accuracy, which verifies the superiority of the proposed method.