基于全局特征的行人重识别算法主要使用交叉熵损失函数和三元组损失函数来监督网络的学习。然而,原始三元组损失函数在增大类间距离的同时并未很好地优化类内距离,为了解决这个问题,提出一种基于全局特征的行人重识别改进算法。该算法是在三元组损失函数的基础上进行改进,即在原始三元组损失函数中引入一项类内距离,使改进后的三元组损失函数能够在增大类间距离的同时减小类内距离。在Market1501、DukeMTMC-reID和CUHK03数据集上进行大量实验,实验结果表明所提算法得到的特征具有更强的判别性,在基于全局特征的模型中可以取得最优的性能,接近甚至超过一些基于局部特征的模型。

Person re-idetntification algorithms based on global features primarily use the cross-entropy loss function and triplet loss function to supervize network learning. However, the original triplet loss function does not optimize an intraclass distance and increases an interclass distance. To solve this problem, an improved person re-idetntification algorithm based on global features is proposed. The algorithm is improved on the basis of the triple loss function, that is, an intraclass distance is introduced into the original triple loss function, so that the improved triple loss can be reduced while increasing the interclass distance intraclass distance. A number of experiments have been conducted on the Market1501, DukeMTMC-reID, and CUHK03 datasets. The experimental results show that the proposed algorithm obtains discriminative features, and a model based on the global features can achieve a performance that approaches or even exceeds some local feature models.