BagDINO: Multi-View Baggage Re-Identification with DINOv3

2026-10-07

AI Summary

This study addresses the absence of visual recognition solutions and the challenge of cross-view matching in airport lost luggage scenarios by proposing a DINOv3-based baggage re-identification method. This work pioneers the application of the DINOv3 foundation model to this task, integrating a BNNeck classification head and employing LoRA for parameter-efficient fine-tuning to overcome feature adaptation under few-shot conditions. Experimental results on the MVB benchmark demonstrate that the proposed approach significantly outperforms conventional feature-freezing strategies. These findings effectively validate the generalization capability of vision foundation models under limited data regimes, achieving stable and efficient cross-view baggage retrieval.

Abstract

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.

Problem

• Baggage Re-Identification
• Instance-level Retrieval
• Multi-View
• Airport Operations

Innovation

• Baggage Re-Identification
• DINOv3
• LoRA
• Parameter-Efficient Fine-Tuning
• BNNeck