BagDINO：基于DINOv3的多视角行李再识别
BagDINO: Multi-View Baggage Re-Identification with DINOv3
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中文总结 AI 辅助
针对机场行李标签缺失时的视觉识别难题，本文提出BagDINO方法，利用DINOv3骨干网络结合BNNeck头和LoRA参数高效适配，在MVB基准上验证了该方法在有限训练数据下实现稳定有效的多视角行李再识别。
中文摘要 AI 辅助
托运行李处理不当仍然是机场运营中反复出现的问题，当前找回流程仍主要依赖基于标签的追踪，当标签证据缺失或不可用时，这种方法无法直接支持视觉识别。本文将行李再识别作为多摄像头场景下的实例级检索问题进行研究，利用DINOv3基础模型表示来将查询图像与注册行李图像库中的图像进行匹配。在DINOv3骨干网络之上放置了一个Torchreid风格的BNNeck再识别头，并通过LoRA进行参数高效适配。实验在MVB基准上使用渐进式研究进行，比较了完全冻结的骨干网络与LoRA和微调策略。结果表明，在有限训练数据下，基础模型特征的参数高效适配为多视角行李再识别提供了一种有效且稳定的方法。
英文摘要
Mishandled checked baggage remains a recurrent issue in airport operations, and current recovery workflows still largely rely on tag-based tracking, which does not directly support visual identification when tag evidence is missing or unavailable. This paper investigates baggage re-identification as an instance-level retrieval problem in a multi-camera setting, leveraging DINOv3 foundation-model representations to match a query image against a gallery of registered baggage images. A Torchreid-style BNNeck re-identification head is placed on top of a DINOv3 backbone, and parameter-efficient adaptation is performed via LoRA. Experiments are conducted on the MVB benchmark using a progressive study that compares a fully frozen backbone against LoRA and fine-tuning strategies. Results indicate that parameter-efficient adaptation of foundation-model features provides an effective and stable approach for multi-view baggage re-identification under limited training data.
发表机构
- University of Bari Aldo Moro（巴里阿尔多莫罗大学）
- SER&Practices（SER&Practices公司）
机构由 AI 辅助整理，请以论文原文为准。
