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QuadNet: Quadruplet loss for multi-view learning in baggage re-identification
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NSTL
Elsevier
Recently, baggage re-identification (ReID) has become an attractive topic in computer vision because it plays an important role in intelligent surveillance. However, the wide variations in different views of baggage items degrade baggage ReID performance. In this paper, a novel QuadNet is proposed to solve the multi-view problem in baggage ReID at three levels. At the sample level, we propose a multi-view sampling strategy which samples hard examples from multiple identities in multiple views. The sampled baggage items are used to construct quadruplets. At the feature level, view-aware attentional local features are extracted from discriminative regions in each view. These local features are fused with global features to obtain better representations of the quadruplets. At the loss level, a multi-view quadruplet loss operating on the representations of quadruplets is proposed to reduce the intra-class distances caused by view variations and increase the inter-class distances of baggage images captured in the same view. A random local blur data augmentation is proposed to handle the motion blur which is often found in baggage images. The multi-task learning of materials is introduced to obtain discriminative features based on the materials of baggage surfaces. Extensive experiments on three ReID datasets, MVB, Market-1501 and VeRi-776, indicate the remarkable effectiveness and good generalization of the QuadNet model. It has achieved the state-of-the-art performance on the three datasets. Crown Copyright (c) 2022 Published by Elsevier Ltd. All rights reserved.
Baggage re-identificationMulti-view learningQuadruplet lossView-aware features
Chu, Xiuxiu、Zhang, Li、Sun, Yunda、Li, Dong、Maybank, Stephen J.、Yang, Hao
NSTL
Elsevier
