基于注意力机制的多级特征级联行人重识别

Multi-Level Features Cascade for Person Re-Identification Based on Attention Mechanism

针对现有行人重识别算法因细节信息关注不足导致的判别性不强问题,提出了一种基于注意力机制的多级特征级联行人重识别算法。首先,通过级联不同深度的特征实现对不同层级特征的充分利用,以补充高层级特征中的细节信息。然后,引入一对互补的注意力机制模块,以融合特征图中相似的像素及通道,弥补特征中的空间位置信息,提高特征的判别性。最后,在Market-1501、DukeMTMC-ReID、CUHK03数据集上进行了大量实验。结果表明,本算法的识别精度和平均准确率优于大多数当前的主流算法。

To address the problem of limited discriminative power in existing person re-identification algorithms owing to the loss of details,a multi-level features cascade for person re-identification algorithm based on attention mechanism is proposed in this paper.First,the algorithm is used to cascade features at different depths to fully utilize the features of various levels and replenish detailed information in high-level feature maps.Then,a pair of complementary attention mechanism modules is introduced to integrate similar pixels and channels in the high-level feature maps,compensate for the space location information in the features,and improve the discriminativeness of the features.Finally,extensive experiments are performed on Market-1501,DukeMTMC-ReID,and CUHK03 data sets.Results show that the algorithm shows better recognition and average accuracies than most current mainstream algorithms.