Video-based Visible-infrared Person Re-identification With Auxiliary Samples
2023 · Yunhao Du, Cheng Lei, Zhicheng Zhao, et al.
Abstract
Visible-infrared person re-identification (VI-ReID) aims to match persons captured by visible and infrared cameras, allowing person retrieval and tracking in 24-hour surveillance systems. Previous methods focus on learning from cross-modality person images in different cameras. However, temporal information and single-camera samples tend to be neglected. To crack this nut, in this paper, we first contribute a large-scale VI-ReID dataset named BUPTCampus. Different from most existing VI-ReID datasets, it 1) collects tracklets instead of images to introduce rich temporal information, 2) contains pixel-aligned cross-modality sample pairs for better modality-invariant learning, 3) provides one auxiliary set to help enhance the optimization, in which each identity only appears in a single camera. Based on our constructed dataset, we present a two-stream framework as baseline and apply Generative Adversarial Network (GAN) to narrow the gap between the two modalities. To exploit the advantage
Authors
(none)
Tags
Stats
Related papers
- Learning Modal-invariant And Temporal-memory For Video-based Visible-infrared Person Re-identification (2022)14.23
- Implicit Discriminative Knowledge Learning For Visible-infrared Person Re-identification (2024)16.19
- BIT: Matching-based Bi-directional Interaction Transformation Network For Visible-infrared Person Re-identification (2026)0.00
- Bridging The Gap: Multi-level Cross-modality Joint Alignment For Visible-infrared Person Re-identification (2023)11.29
- Dynamic Dual-attentive Aggregation Learning For Visible-infrared Person Re-identification (2020)19.67
- Mutual Information Guided Optimal Transport For Unsupervised Visible-infrared Person Re-identification (2024)0.00
- Unified Batch All Triplet Loss For Visible-infrared Person Re-identification (2021)9.03
- Bridging The Distribution Gap Of Visible-infrared Person Re-identification With Modality Batch Normalization (2021)8.60