语义引导的遮挡行人再识别注意力网络

任雪娜; 张冬明; 包秀国; 李冰

doi:10.11959/j.issn.1000-436x.2021184

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语义引导的遮挡行人再识别注意力网络

学术论文 | 更新时间：2024-06-05

- 语义引导的遮挡行人再识别注意力网络
- Semantic guidance attention network for occluded person re-identification
- 通信学报 2021年42卷第10期页码：106-116
- 作者机构：
  
  1. 中国科学院信息工程研究所，北京 100093
  2. 中国科学院大学网络空间安全学院，北京 100093
  3. 国家计算机网络应急技术处理协调中心，北京 100029
  4. 北京航空航天大学自动化学院，北京 100191
- 作者简介：
  
  [ "任雪娜（1989- ），女，河北石家庄人，中国科学院信息工程研究所博士生，主要研究方向为行人重识别（遮挡行人识别、变装行人识别等）" ]
  [ "张冬明（1977- ），男，江苏盐城人，博士，国家计算机网络应急技术处理协调中心研究员、博士生导师，主要研究方向为多媒体内容检索、模式识别、视频编码等" ]
  [ "包秀国（1963- ），男，江苏如皋人，博士，国家计算机网络应急技术处理协调中心教授级高级工程师、博士生导师，主要研究方向为网络与信息安全" ]
  [ "李冰（1990- ），男，辽宁沈阳人，北京航空航天大学博士生，主要研究方向为压缩视频行为识别" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2018YFB0804704);国家自然科学基金资助项目(61672495);国家自然科学基金资助项目(U1736218)
- DOI：10.11959/j.issn.1000-436x.2021184
  中图分类号： TN92
- 网络出版日期：2021-10，
  
  纸质出版日期：2021-10-25
- 稿件说明：
移动端阅览
任雪娜, 张冬明, 包秀国, 等. 语义引导的遮挡行人再识别注意力网络[J]. 通信学报, 2021,42(10):106-116.

Xuena REN, Dongming ZHANG, Xiuguo BAO, et al. Semantic guidance attention network for occluded person re-identification[J]. Journal on communications, 2021, 42(10): 106-116.
任雪娜, 张冬明, 包秀国, 等. 语义引导的遮挡行人再识别注意力网络[J]. 通信学报, 2021,42(10):106-116. DOI： 10.11959/j.issn.1000-436x.2021184.

Xuena REN, Dongming ZHANG, Xiuguo BAO, et al. Semantic guidance attention network for occluded person re-identification[J]. Journal on communications, 2021, 42(10): 106-116. DOI： 10.11959/j.issn.1000-436x.2021184.

摘要

为了解决遮挡场景下行人再识别的特征不对齐、错误匹配的问题，提出了一种语义引导对齐的注意力网络（SGAN）对齐行人的不同部分。SGAN 以行人的语义掩膜作为监督信息，通过全局语义引导和局部语义引导提取行人的全身和局部特征，并根据人体不同部分的可见性动态调整模型训练。在推理阶段，依据注意力模型获得局部区块的可见性，利用共享可见的人体部分的匹配策略自适应地对特征进行相似度的计算。实验结果表明， SGAN能够容忍一定的遮挡，它的准确率不仅在全身数据集上优于大多数先进模型，在2个较大规模的复杂遮挡数据集Occluded-DukeMTMC和P-DukeMTMC-reID上也优于现有的行人再识别方法。

Abstract

To solve the problem of misalignment and mismatch in occluded person Re-ID

SGAN (semantic guided attention network) was proposed.In SGAN

the semantic masks of pedestrians were used as supervision to learn the global and local features through the attention modules

and the training process was dynamically adjusted according to the visibility of local regions.In the inference stage

the part-to-part matching strategy was adopted to adaptively measure visible features based on the feature visibility

which was obtained based on the learned masks from the attention modules.Experimental results show that the average accuracy of SGAN on the holistic datasets is better than most advanced models.Additionally

it is tolerant of occlusions and largely outperforms existing person Re-ID methods on two larger-scale complex occlusion datasets (Occluded-DukeMTMC and P-DukeMTMC-reID).

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references

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