arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2602.07565cs.CV
收藏

远程人类识别：挑战、方法与HID 2025竞赛结果

Human Identification at a Distance: Challenges, Methods and Results on the Competition HID 2025

Shenzhen Polytechnic University（深圳职业技术大学）；Fujitsu Research and Development Center Company Ltd.（富士通研发有限公司）；Beijing Jiaotong University（北京交通大学）；South China University of Technology（华南理工大学）；Shandong University（山东大学）；EVERSPRY；Wuhan University of Technology（武汉理工大学）；Sun Yat-sen University（中山大学）；Shanghai Jiao Tong University（上海交通大学）；Fujitsu Ltd.（富士通株式会社）；Huazhong University of Science and Technology（华中科技大学）；Hefei University of Technology（合肥工业大学）；Peking University, Shenzhen Graduate School（北京大学深圳研究生院）；Beijing Normal University（北京师范大学）；Watrix Technology Limited Co. Ltd.（华智科技有限公司）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Cordoba（科尔多瓦大学）；University of East London（东伦敦大学）；Southern University of Science and Technology（南方科技大学）
Jingzhe Ma, Meng Zhang, Jianlong Yu, Kun Liu, Zunxiao Xu, Xue Cheng, Junjie Zhou, Yanfei Wang, Jiahang Li, Zepeng Wang, Kazuki Osamura, Rujie Liu, Narishige Abe… 展开作者

Jingzhe Ma, Meng Zhang, Jianlong Yu, Kun Liu, Zunxiao Xu, Xue Cheng, Junjie Zhou, Yanfei Wang, Jiahang Li, Zepeng Wang, Kazuki Osamura, Rujie Liu, Narishige Abe, Jingjie Wang, Shunli Zhang, Haojun Xie, Jiajun Wu, Weiming Wu, Wenxiong Kang, Qingshuo Gao, Jiaming Xiong, Xianye Ben, Lei Chen, Lichen Song, Junjian Cui, Haijun Xiong, Junhao Lu, Bin Feng, Mengyuan Liu, Ji Zhou, Baoquan Zhao, Ke Xu, Yongzhen Huang, Liang Wang, Manuel J Marin-Jimenez, Md Atiqur Rahman Ahad, Shiqi Yu

阅读 PDF
arXiv 页面 ↗HTML 原文 ↗

AI总结：

HID 2025竞赛评估了步态识别在远程人类识别中的挑战与进展，通过SUSTech-Competition数据集验证了算法性能的提升，达到94.2%的准确率。

摘要：

远程人类识别（HID）具有挑战性，因为传统生物特征模态如面部和指纹在现实场景中往往难以获取。步态识别提供了一个实用的替代方案，因为它可以在远处可靠地捕捉到。为了促进步态识别的进步并提供一个公平的评估平台，自2020年起已每年组织国际远程人类识别竞赛（HID）。自2023年起，竞赛采用了具有显著服装、携带物品和视角变化的挑战性SUSTech-Competition数据集。没有专门的训练数据提供，要求参赛者使用外部数据集训练模型。每年，竞赛都会应用不同的随机种子生成不同的评估分割，从而降低过拟合的风险并支持对跨领域泛化能力的公平评估。尽管HID 2023和HID 2024已经使用了这个数据集，HID 2025明确检验了算法进步是否能超越之前观察到的准确性限制。尽管难度增加，参赛者实现了进一步的改进，最佳方法达到了94.2%的准确率，设定了该数据集的新基准。我们还分析了关键技术趋势，并概述了步态识别未来研究的潜在方向。

英文摘要：

Human identification at a distance (HID) is challenging because traditional biometric modalities such as face and fingerprints are often difficult to acquire in real-world scenarios. Gait recognition provides a practical alternative, as it can be captured reliably at a distance. To promote progress in gait recognition and provide a fair evaluation platform, the International Competition on Human Identification at a Distance (HID) has been organized annually since 2020. Since 2023, the competition has adopted the challenging SUSTech-Competition dataset, which features substantial variations in clothing, carried objects, and view angles. No dedicated training data are provided, requiring participants to train their models using external datasets. Each year, the competition applies a different random seed to generate distinct evaluation splits, which reduces the risk of overfitting and supports a fair assessment of cross-domain generalization. While HID 2023 and HID 2024 already used this dataset, HID 2025 explicitly examined whether algorithmic advances could surpass the accuracy limits observed previously. Despite the heightened difficulty, participants achieved further improvements, and the best-performing method reached 94.2% accuracy, setting a new benchmark on this dataset. We also analyze key technical trends and outline potential directions for future research in gait recognition.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

↑