Visible Thermal Person Re-Identification via Dual-Constrained Top-Ranking

By Mang Ye, Zheng Wang, Xiangyuan Lan, Pong Chi Yuen. International Joint Conference on Artificial Intelligence, 2018

408 citations recorded by the source corpus.

Cross-modality person re-identification between the thermal and visible domains is extremely important for night-time surveillance applications. Existing works in this filed mainly focus on learning sharable feature representations to handle the cross-modality discrepancies. However, besides the cross-modality discrepancy caused by different camera spectrums, visible thermal person re-identification also suffers from large cross-modality and intra-modality variations caused by different camera views and human poses. In this paper, we propose a dual-path network with a novel bi-directional dual-constrained top-ranking loss to learn discriminative feature representations. It is advantageous in two aspects: 1) end-to-end feature learning directly from the data without extra metric learning steps, 2) it simultaneously handles the cross-modality and intra-modality variations to ensure the discriminability of the learnt representations. Meanwhile, identity loss is further incorporated to model the identity-specific information to handle large intra-class variations. Extensive experiments on two datasets demonstrate the superior performance compared to the state-of-the-arts.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/8cf0933ec62c06c9ef8ee0a8d28930f71fef3f91

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Citations in the corpus

Infrared-Visible Cross-Modal Person Re-Identification with an X Modality (2020, methodology): “For statistically stable results, the evaluation procedure is repeated for 10 trials with randomly sampled query and gallery sets, as (Ye et al. 2018a; Feng, Lai, and Xie 2019; Wang et al. 2019c).”
Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification (2019, methodology): “Recently, adversarial learning [2] or metric learning methods [39, 9, 38] are applied to learn the feature representation of heterogeneous person images involving intra-modality and cross-modality variations.”
Robust Anchor Embedding for Unsupervised Video Person re-IDentification in the Wild (2018, methodology): “Moreover, some works combine them together to improve the performance [42,48].”
Deep Learning for Person Re-Identification: A Survey and Outlook (2020, background): “It is important in low-lighting conditions, where the images can only be captured by infrared cameras [20], [51], [189].”
RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature Alignment (2019, methodology): “To deal with the above issues, existing cross-modality re-id methods [32, 37, 38, 3, 9] mainly focus on bridging the gap between the RGB and IR images via feature alignment as shown in Figure 1.”
Learning to Reduce Dual-Level Discrepancy for Infrared-Visible Person Re-Identification (2019, methodology): “(a-b) are obtained using BDTR [23] on the RegDB datasetm which uses only feature-level constraints; (c-d) are obtained by our method.”
Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-Identification (2020, methodology): “Ye et al. utilize a dual-path network with a bi-directional dual-constrained top-ranking loss (Ye et al. 2018a) and modality-speciﬁc and modality-shared metrics (Ye et al. 2018b).”
Bi-Directional Center-Constrained Top-Ranking for Visible Thermal Person Re-Identification (2020, background): “A preliminary conference version [20] has been published in IJCAI-ECAI 2018.”
Cross-Modality Person Re-Identification With Shared-Specific Feature Transfer (2020, methodology): “In this subsection, we compare our proposed algorithm with the baselines as well as the state-of-the-art methods, including Zero-Padding [46], TONE [49], BDTR [50], cm-GAN [4], D 2 RL[44], MSR[7], D-HSME[13], IPVT[19], JSIA-ReID[57] and AlignGAN[42].”