基于渐进式注意力金字塔的行人重识别方法

Person Re-identification Method Based on Progressive Attention Pyramid

针对现有行人重识别算法对行人特征提取不充分,导致算法在行人遮挡、姿态变化等场景下准确度较低的问题,提出了基于渐进式注意力金字塔的行人重识别方法。该方法基于注意力机制设计了一种渐进式的特征金字塔结构,将通道和空间两种注意力模块嵌入特征金字塔结构中,并分别应用在特征的通道和空间两个维度上,通道注意力金字塔聚合骨干网络各层级不同通道维度中值得关注的特征,空间注意力金字塔提取不同空间维度中值得关注的特征。金字塔的每一级都按照“切分-关注-合并”的原则,自底向上不断学习行人特征图在不同切分等级下的注意力,让网络充分挖掘到来自不同通道维度和不同空间维度的关键特征。同时,通过级联结构和可变形卷积实现多层级特征对齐,进一步提高模型的重识别精度。分别在Market-1501和DukeMTMC-reID两个主流数据集上对该方法进行实验,实验结果表明该方法可以让模型关注到更丰富的行人特征,模型的Rank-1指标相比基准网络分别提高了3.2%和5.8%,mAP指标分别提高了6.8%和6.6%。

Aiming at the problem that the existing person re-identification algorithms do not fully extract person features,resulting in low accuracy of the algorithm in scenes such as person occlusion and posture change,a person re-identification method based on progressive attention pyramid is proposed.This method designs a progressive feature pyramid structure based on the attention mechanism,embeds the channel and spatial attention modules into the feature pyramid structure,and applies them to the channel and spatial dimensions of the feature.Channel attention pyramid aggregates the noteworthy features in different channel dimensions at each level of the backbone network,and the spatial attention pyramid extracts the noteworthy features in different spatial dimensions.Each level of the pyramid follows the principle of“split-attend-concat”,and continuously learns the person feature map under different segmentation levels from the bottom up.Attention allows the network to fully mine key features from different channel dimensions and different spatial dimensions.At the same time,the multi-level feature alignment is realized through the cascade structure and deformable convolution,which further improves the re-identification accuracy of the model.In this paper,the method is tested on two mainstream datasets,Market-1501 and DukeMTMC-reID,respectively.Experimental results show that this method can allow the model to focus on richer person features.Compared with the baseline network,the Rank-1 index of the model increases by 3.2%and 5.8%,and the mAP index increases by 6.8%and 6.6%,respectively.