# MVB: A Large-Scale Dataset for Baggage Re-Identification and Merged Siamese Networks

## TL;DR

This paper introduces MVB, a large-scale multi-view baggage dataset with surface labels, designed to improve baggage re-identification, and proposes a merged Siamese network as a baseline model for this task.

## Contribution

The paper presents the first large-scale baggage ReID dataset with multi-view images and surface labels, and introduces a merged Siamese network baseline for the task.

## Key findings

- MVB contains 4519 baggage identities and 22660 images.
- The merged Siamese network achieves promising results on MVB.
- Multi-view and surface labels improve baggage ReID performance.

## Abstract

In this paper, we present a novel dataset named MVB (Multi View Baggage) for baggage ReID task which has some essential differences from person ReID. The features of MVB are three-fold. First, MVB is the first publicly released large-scale dataset that contains 4519 baggage identities and 22660 annotated baggage images as well as its surface material labels. Second, all baggage images are captured by specially-designed multi-view camera system to handle pose variation and occlusion, in order to obtain the 3D information of baggage surface as complete as possible. Third, MVB has remarkable inter-class similarity and intra-class dissimilarity, considering the fact that baggage might have very similar appearance while the data is collected in two real airport environments, where imaging factors varies significantly from each other. Moreover, we proposed a merged Siamese network as baseline model and evaluated its performance. Experiments and case study are conducted on MVB.