Research on person re-identification algorithm based on multi-task learning
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 surve...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2024-06-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024157/ |
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| _version_ | 1841528864023511040 |
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| author | MI Rongxin YAO Wenwen WU Binghao |
| author_facet | MI Rongxin YAO Wenwen WU Binghao |
| author_sort | MI Rongxin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7e1401fed83c4d63b048a2e0ebadce60 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2024-06-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-7e1401fed83c4d63b048a2e0ebadce602025-01-15T03:33:35ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012024-06-014012713664852541Research on person re-identification algorithm based on multi-task learningMI RongxinYAO WenwenWU BinghaoPerson 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.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024157/person re-identificationintelligent video surveillancerepresentation learningmetric learningmulti-task learning |
| spellingShingle | MI Rongxin YAO Wenwen WU Binghao Research on person re-identification algorithm based on multi-task learning Dianxin kexue person re-identification intelligent video surveillance representation learning metric learning multi-task learning |
| title | Research on person re-identification algorithm based on multi-task learning |
| title_full | Research on person re-identification algorithm based on multi-task learning |
| title_fullStr | Research on person re-identification algorithm based on multi-task learning |
| title_full_unstemmed | Research on person re-identification algorithm based on multi-task learning |
| title_short | Research on person re-identification algorithm based on multi-task learning |
| title_sort | research on person re identification algorithm based on multi task learning |
| topic | person re-identification intelligent video surveillance representation learning metric learning multi-task learning |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024157/ |
| work_keys_str_mv | AT mirongxin researchonpersonreidentificationalgorithmbasedonmultitasklearning AT yaowenwen researchonpersonreidentificationalgorithmbasedonmultitasklearning AT wubinghao researchonpersonreidentificationalgorithmbasedonmultitasklearning |