基于无参注意力和联合损失的行人重识别

程述立; 汪烈军; 王有丹

doi:10.13568/j.cnki.651094.651316.2022.07.09.0001

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基于无参注意力和联合损失的行人重识别

更新时间：2026-01-21

- 基于无参注意力和联合损失的行人重识别
- Journal of Xinjiang University (Natural Science Edition in Chinese and English) Vol. 40, Issue 2, Pages: 202-209(2023)
- 作者机构：
  
  1. 新疆大学信息科学与工程学院
  2. 新疆大学数学与系统科学学院
- 作者简介：
- 基金信息：
- DOI：10.13568/j.cnki.651094.651316.2022.07.09.0001
  CLC： TP391.41
- Published：2023
- 稿件说明：
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[1]程述立,汪烈军,王有丹.基于无参注意力和联合损失的行人重识别[J].新疆大学学报(自然科学版)(中英文),2023,40(02):202-209.
[1]程述立,汪烈军,王有丹.基于无参注意力和联合损失的行人重识别[J].新疆大学学报(自然科学版)(中英文),2023,40(02):202-209. DOI： 10.13568/j.cnki.651094.651316.2022.07.09.0001.

DOI：10.13568/j.cnki.651094.651316.2022.07.09.0001.

摘要

现阶段行人重识别一般只考虑二维特征，将各个特征点统一处理，存在特征提取不足的问题，故提出基于无参注意力的行人重识别（PFNet）来解决上述问题．该模型在Res Net-50网络上进行改进，分别在第一个残差块和第三个残差块后引入无参注意力机制，该注意力机制能根据图片本身特点赋予各特征点合适的权重，可以保留更丰富的信息特征且不会引入额外参数．接着使用自适应平均池化层保留主要特征且捕捉特定域的判别特征，然后使用ID损失、三元组损失和自适应加权排序损失的联合损失函数来训练模型．算法在Market-1501、DukeMTMC-reID和CUHK03三个主流的行人重识别数据集上的首位命中率分别达到95.5%、90.9%和84.3%，平均精度均值分别达到89.6%、81.6%和82.0%．实验结果表明，使用注意力和联合损失函数的策略可以提高模型精度．

Abstract

At present

pedestrian re-recognition usually only considers two-dimensional features and deals with each feature point uniformly

which has the problem of insufficient feature extraction. In this paper

pedestrian re-recognition based on parameter-free attention(PFNet) is proposed to solve the above problems. The model is improved on Res Net-50 network

and the non-parametric attention mechanism is introduced after the first residual block and the third residual block respectively. The attention mechanism can assign appropriate weight to each feature point according to the characteristics of the image itself

and can retain richer information features without introducing additional parameters. Then

the adaptive average pooling layer is used to retain the main features and capture the discriminant features of a specific domain. Then

the model is trained with a combined loss function of ID loss

triplet loss and adaptive weighted ranking loss. The algorithm achieves 95.5%

90.9% and 84.3%

respectively; On the three mainstream pedestrian re-recognition datasets of Market-1501

DukeMTMC-reID and CUHK03

and the average accuracy is 89.6%

81.6% and 82.0%

respectively. Experimental results show that the strategy using attention and joint loss function can improve the model accuracy.

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references

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