RGB-Infrared Cross-Modality Person Re-identification

Wu, Ancong; Zheng, Wei‐Shi; Yu, Hang; Gong, Shaogang; Lai, Jianhuang

doi:10.1109/iccv.2017.575

Cited by 506 publications

(577 citation statements)

References 49 publications

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“…To our best knowledge, in the literature there are only five pioneer works about visible-thermal person Re-ID. RGB-Infrared Cross-Modality Person Re-Identification [27]. Wu et al firstly proposed to address the the RGB-IR cross-modality Re-ID problem and contributed a new multiple modality person Re-ID dataset named SYSU-MM01.…”

Section: Related Workmentioning

confidence: 99%

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Enhancing the discriminative feature learning for visible-thermal cross-modality person re-identification

et al. 2020

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Existing person re-identification has achieved great progress in the visible domain, capturing all the person images with visible cameras. However, in a 24-hour intelligent surveillance system, the visible cameras may be noneffective at night. In this situation, thermal cameras are the best supplemental components, which capture images without depending on visible light. Therefore, in this paper, we investigate the visible-thermal cross-modality person re-identification (VT Re-ID) problem. In VT Re-ID, there are two knotty problems should be well handled, cross-modality discrepancy and intra-modality variations. To address these two issues, we propose focusing on enhancing the discriminative feature learning (EDFL) with two extreme simple means from two core aspects, (1) skip-connection for mid-level features incorporation to improve the person features with more discriminability and robustness, and (2) dual-modality triplet loss to guide the training procedures by simultaneously considering the cross-modality discrepancy and intra-modality variations. Additionally, the two-stream CNN structure is adopted to learn the multi-modality sharable person features. The experimental results on two datasets show that our proposed EDFL approach distinctly outperforms state-of-the-art methods by large margins, demonstrating the effectiveness of our EDFL to enhance the discriminative feature learning for VT Re-ID.

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Section: Related Workmentioning

confidence: 99%

“…Therefore, it is necessary to study the cross-modality person Re-ID problem. In this paper, we address the visible-thermal person Re-Identification (VT Re-ID) in a 24-hour intelligent surveillance system, which is also termed as RGB-infrared person Re-ID [27].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the discriminative feature learning for visible-thermal cross-modality person re-identification

et al. 2020

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“…In addition, the spectrum between IR and *Corresponding author Email address:yangdxng100@126.com (Zhao Yang) RGB images is different. So, the method for RGB-based person Re-ID can not be adopted in RGB-IR cross-modality person Re-ID problem effectively [8]. To address this issue, some methods have been proposed in this field.…”

Section: Introductionmentioning

confidence: 99%

“…To address this issue, some methods have been proposed in this field. Wu et al [8] released a large-scale cross-modality person Re-ID dataset and proposed a deep one-stream architecture named zero-padding network. In the training stage of zero-padding network, Cross-Entropy (CE) loss function is used to supervise the network.…”

Section: Introductionmentioning

confidence: 99%

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Hetero-Center loss for cross-modality person Re-identification

et al. 2020

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Cross-modality person re-identification is a challenging problem which retrieves a given pedestrian image in RGB modality among all the gallery images in infrared modality. The task can address the limitation of RGB-based person Re-ID in dark environments. Existing researches mainly focus on enlarging inter-class differences of feature to solve the problem. However, few studies investigate improving intra-class cross-modality similarity, which is important for this issue. In this paper, we propose a novel loss function, called Hetero-Center loss (HC loss) to reduce the intra-class cross-modality variations. Specifically, HC loss can supervise the network learning the cross-modality invariant information by constraining the intra-class center distance between two heterogenous modalities. With the joint supervision of Cross-Entropy (CE) loss and HC loss, the network is trained to achieve two vital objectives, inter-class discrepancy and intra-class cross-modality similarity as much as possible. Besides, we propose a simple and high-performance network architecture to learn local feature representations for cross-modality person re-identification, which can be a baseline for future research. Extensive experiments indicate the effectiveness of the proposed methods, which outperform state-of-the-art methods by a wide margin.

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Multi‐feature subspace representation network for person re‐identification via bird's‐eye view image

Zhang

Wu²,

Chen³

et al. 2023

Computer Animation & Virtual

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Person re‐identification (Re‐ID) is one of the most remarkable research topics that widely applied in our daily lives. For person Re‐ID in bird's eye scenes, traditional computer vision‐based methods used multiple features, for example, texture and color, of a pedestrian's head and shoulders. Those methods are difficult to cope with environments of variety and the change the appearance of different people due to the instability of feature detection. On the other hand, although recent advanced deep learning‐based methods are powerful to extract discriminative features, the requirement of a large amount of annotated training data restricts the appliable tasks. To overcome this problem, in this article, we propose a novel method fusing multiple heterogeneous features through a multi‐feature subspace representation network (MFSRN) to maximize the classification performance while keeping the disparity among features as small as possible, that is, common‐subspace constraints. We conducted comparative experiments with state‐of‐the‐art models on the bird's‐eye view person dataset, and extensive experimental results demonstrated that our proposed MFSRN could achieve better recognition performance. Furthermore, the validity and stability of the method are confirmed.

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RGB-Infrared Cross-Modality Person Re-identification

Cited by 506 publications

References 49 publications

Enhancing the discriminative feature learning for visible-thermal cross-modality person re-identification

Enhancing the discriminative feature learning for visible-thermal cross-modality person re-identification

Hetero-Center loss for cross-modality person Re-identification

Multi‐feature subspace representation network for person re‐identification via bird's‐eye view image

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