MVB: A Large-Scale Dataset for Baggage Re-Identification and Merged Siamese Networks
TL
TL;DR
MVB (Multi View Baggage) 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 and has remarkable inter-class similarity and intra-class dissimilarity.
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.
References21 items
1
Learning Discriminative Features with Multiple Granularities for Person Re-Identification
2
Beyond Part Models: Person Retrieval with Refined Part Pooling
3
Person Transfer GAN to Bridge Domain Gap for Person Re-identification
4
Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification
5