Fine-grained Unsupervised Domain Adaptation for Gait Recognition

Kang Ma, Ying Fu*, Dezhi Zheng, Yunjie Peng, Chunshui Cao, Yongzhen Huang*

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

6 引用 (Scopus)

摘要

Gait recognition has emerged as a promising technique for the long-range retrieval of pedestrians, providing numerous advantages such as accurate identification in challenging conditions and non-intrusiveness, making it highly desirable for improving public safety and security. However, the high cost of labeling datasets, which is a prerequisite for most existing fully supervised approaches, poses a significant obstacle to the development of gait recognition. Recently, some unsupervised methods for gait recognition have shown promising results. However, these methods mainly rely on a fine-tuning approach that does not sufficiently consider the relationship between source and target domains, leading to the catastrophic forgetting of source domain knowledge. This paper presents a novel perspective that adjacent-view sequences exhibit overlapping views, which can be leveraged by the network to gradually attain cross-view and cross-dressing capabilities without pre-training on the labeled source domain. Specifically, we propose a fine-grained Unsupervised Domain Adaptation (UDA) framework that iteratively alternates between two stages. The initial stage involves offline clustering, which transfers knowledge from the labeled source domain to the unlabeled target domain and adaptively generates pseudo-labels according to the expressiveness of each part. Subsequently, the second stage encompasses online training, which further achieves cross-dressing capabilities by continuously learning to distinguish numerous features of source and target domains. The effectiveness of the proposed method is demonstrated through extensive experiments conducted on widely-used public gait datasets.

源语言英语
主期刊名Proceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
出版商Institute of Electrical and Electronics Engineers Inc.
11279-11288
页数10
ISBN(电子版)9798350307184
DOI
出版状态已出版 - 2023
活动2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023 - Paris, 法国
期限: 2 10月 20236 10月 2023

出版系列

姓名Proceedings of the IEEE International Conference on Computer Vision
ISSN(印刷版)1550-5499

会议

会议2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
国家/地区法国
Paris
时期2/10/236/10/23

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引用此

Ma, K., Fu, Y., Zheng, D., Peng, Y., Cao, C., & Huang, Y. (2023). Fine-grained Unsupervised Domain Adaptation for Gait Recognition. 在 Proceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023 (页码 11279-11288). (Proceedings of the IEEE International Conference on Computer Vision). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCV51070.2023.01039