Angular Triplet Loss-based Camera Network For Reid

Abstract

Person re-identification (ReID) is a challenging crosscamera retrieval task to identify pedestrians. Many complex network structures are proposed recently and many of them concentrate on multi-branch features to achieve high performance. However, they are too heavy-weight to deploy in realworld applications. Additionally, pedestrian images are often captured by different surveillance cameras, so the varied lights, perspectives and resolutions result in inevitable multi-camera domain gaps for ReID. To address these issues, this paper proposes ATCN, a simple but effective angular triplet loss-based camera network, which is able to achieve compelling performance with only global features. In ATCN, a novel angular distance is introduced to learn a more discriminative feature representation in the embedding space. Meanwhile, a lightweight camera network is designed to transfer global features to more discriminative features. ATCN is designed to be simple and flexible so it can be easily dep

Stats

• citations2
• S2 citations—
• github stars0
• HF likes0
• heat score3.58
• arxiv keyli2020angular

Related papers

• Triplet-based Deep Similarity Learning For Person Re-identification (2018)10.61
• Auto-reid: Searching For A Part-aware Convnet For Person Re-identification (2019)17.27
• Cross-entropy Adversarial View Adaptation For Person Re-identification (2019)12.93
• A Discriminatively Learned CNN Embedding For Person Re-identification (2016)20.32
• Pedestrian Alignment Network For Large-scale Person Re-identification (2017)18.71
• DART\(^3\): Leveraging Distance For Test Time Adaptation In Person Re-identification (2025)0.00
• Ranking And Classification Driven Feature Learning For Person Re_identification (2019)4.01
• Advancing Person Re-identification: Tensor-based Feature Fusion And Multilinear Subspace Learning (2023)2.26