基于多任务学习的行人重识别算法研究

秘蓉新; 姚文文; 吴兵灏

doi:10.11959/j.issn.1000-0801.2024157

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基于多任务学习的行人重识别算法研究

研究与开发 | 更新时间：2024-08-05

- 基于多任务学习的行人重识别算法研究
- Research on person re-identification algorithm based on multi-task learning
- 电信科学 2024年40卷第6期页码：127-136
- 作者机构：
  
  1.国家计算机网络应急技术处理协调中心，北京 100029
  2.北京邮电大学计算机学院，北京 100876
- 作者简介：
  
  [ "秘蓉新（1984- ），女，国家计算机网络应急技术处理协调中心助理研究员，主要研究方向为人工智能、网络安全、信息安全。" ]
  [ "姚文文（1984- ），女，国家计算机网络应急技术处理协调中心助理研究员，主要研究方向为网络安全战略、信息科技发展态势。" ]
  [ "吴兵灏（2001- ），男，北京邮电大学计算机学院（国家示范性软件学院）硕士生，主要研究方向为深度学习、计算机视觉与行人重识别。" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2024157
  中图分类号： TN929.53
- 收稿日期：2024-03-27，
  
  修回日期：2024-05-10，
  
  纸质出版日期：2024-06-20
- 稿件说明：
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秘蓉新,姚文文,吴兵灏.基于多任务学习的行人重识别算法研究[J].电信科学,2024,40(06):127-136.

MI Rongxin,YAO Wenwen,WU Binghao.Research on person re-identification algorithm based on multi-task learning[J].Telecommunications Science,2024,40(06):127-136.
秘蓉新,姚文文,吴兵灏.基于多任务学习的行人重识别算法研究[J].电信科学,2024,40(06):127-136. DOI： 10.11959/j.issn.1000-0801.2024157.

MI Rongxin,YAO Wenwen,WU Binghao.Research on person re-identification algorithm based on multi-task learning[J].Telecommunications Science,2024,40(06):127-136. DOI： 10.11959/j.issn.1000-0801.2024157.

摘要

行人重识别（person re-identification，re-ID）在多摄像机之间进行跨镜检索以匹配目标行人图像，可以在人脸、指纹等生物特征失效的情况下实现行人关联，已成为智能视频监控系统的关键技术，对智能安防、智慧城市等领域的产业落地进行了有效赋能。传统的行人重识别算法通常采用表征学习或度量学习方法。基于多任务学习的机器学习模式，结合表征学习与度量学习方法，综合利用特征表示和距离度量两方面的优势，采用分类损失和三元组损失共同训练模型，使模型在特征提取和相似性度量上都得到充分的训练。实验结果表明，该方法在行人重识别任务中取得了更好的性能，验证了鲁棒性和在泛化能力方面的优越性。

Abstract

Person re-identification (re-ID) involves the cross-camera retrieval and matching of target pedestrian images

facilitating pedestrian association in scenarios where biometric features such as faces and fingerprints may prove ineffective. It has become a pivotal technology in intelligent video surveillance systems

playing a crucial role in domains like smart security and smart cities. Traditional re-ID algorithms typically employ either representation learning or metric learning methods. A novel approach was proposed which combined representation learning and metric learning methods based on the multi-task learning machine learning paradigm. By capitalizing on the advantages of both feature representation and distance metric

and concurrently training the model using classification loss and triplet loss

comprehensive training for both feature extraction and similarity measurement was ensured. Experimental results validate the effectiveness of the proposed approach

demonstrating superior performance in re-ID tasks and underscoring the robustness and superior generalization capability.

关键词

Keywords

references

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