Person Re-identification Research Articles

3D Person Re-Identification Based on Global Semantic Guidance and Local Feature Aggregation

3D Person Re-Identification Based on Global Semantic Guidance and Local Feature Aggregation

Erasing, Transforming, and Noising Defense Network for Occluded Person Re-Identification

Erasing, Transforming, and Noising Defense Network for Occluded Person Re-Identification

Bridge Gap in Pixel and Feature Level for Cross-Modality Person Re-Identification

Bridge Gap in Pixel and Feature Level for Cross-Modality Person Re-Identification

Lightweight Learning for Partial and Occluded Person Re-Identification

Lightweight Learning for Partial and Occluded Person Re-Identification

Reliable Cross-Camera Learning in Random Camera Person Re-Identification

Reliable Cross-Camera Learning in Random Camera Person Re-Identification

Generalized Intra-Camera Supervised Person Re-Identification

Generalized Intra-Camera Supervised Person Re-Identification

Discovering attention-guided cross-modality correlation for visible–infrared person re-identification

Visible–infrared person re-identification (VI Re-ID) is an essential and challenging task. Existing studies mainly focus on learning the unified modality-invariant representations directly from visible and infrared images. However, it is hard to obtain the identity-aware patterns due to the co-existence of inter- and intra-modality discrepancies. In this paper, we propose a novel attention-guided cross-modality correlation method (AGCC) to achieve the modality-invariant and identity-discriminative representations for visible–infrared person Re-ID. Specifically, we introduce a modality-aware attention (MAA) mechanism to model the inter- and intra-modality variations, which generates attention masks of two modalities for preserving the most significant region and obtaining the discriminative patterns in each identity. Further, we present an attention-guided channel and spatial correlation scheme (AGCSC) to establish the attention-guided cross-modality correlation, which can bridge the gap between inter- and intra-modalities. Moreover, a novel joint-modality learning head (JMLH) is developed to promote the metric and mutual learning from both feature distribution and classification logit levels. Extensive experiments on two public SYSU-MM01 and RegDB datasets demonstrate the remarkable superiority of our method over the state of the arts. The implementation codes will be made available soon.

Cross-Domain Person Re-Identification Based on Feature Fusion Invariance

Cross-domain person re-identification is a technique for identifying the same individual across different cameras or environments that necessitates the overcoming of challenges posed by scene variations, which is a primary challenge in person re-identification and a bottleneck for its practical applications. In this paper, we learn the invariance model of cross-domain feature fusion in a labeled source domain and an unlabeled target domain. First, our method learns the global and local fusion features of a person in the source domain by means of supervised learning with no component label and only person identification and obtains the fusion features of the person in the source and target domains by means of unsupervised learning. Based on person fusion features, this paper introduces feature memory to store the fused target features and designs a cross-domain invariance loss function to improve the cross-domain adaptability of the person. Finally, this paper carries out cross-domain person re-identification verification experiments between the Market-1501 and DukeMTMC-reID datasets; the experimental results show that the proposed method achieves significant performance improvement in cross-domain person re-identification.

A novel image restoration solution for cross-resolution person re-identification

A novel image restoration solution for cross-resolution person re-identification

Situational diversity in video person re-identification: introducing MSA-BUPT dataset

Thanks to the success of deep learning over the past few years, the video person re-identification (ReID) algorithms have achieved high accuracy on multiple public benchmark datasets. However, the available video person ReID datasets cover a limited range of real-world scenarios, and they have several obvious limitations: limited camera viewing angles, tiny variations of the shooting scene, and even errors in manual labels. These disadvantages prevent video person ReID from being widely used in real-life scenarios. In this work, a new high-quality multi-situation video person ReID dataset, named MSA-BUPT, is built to promote the video person ReID task in large-scale urban surveillance. Specifically, MSA-BUPT contains 684 identities, 2,665 trajectories, and nearly 250,000 frames from 200-h videos across various complex scenarios. Person attribute annotations and unannotated video data are also provided for other research perspectives, such as cross-modality ReID, cross-domain ReID, and so on. Furthermore, two plug-and-play components are used to improve retrieval capabilities: a new scenario-based data augmentation method is proposed to alleviate the person misalignment problem; a re-ranking strategy based on person attribute is applied to make secondary adjustments to the content to the results of the model. The extensive experimental results show that the above methods improve the performance of some representative state-of-the-art models on the new dataset.

Multi-scale spatio-temporal feature adaptive aggregation for video-based Person Re-identification

Video-based person re-identification (Re-ID) aims to retrieve the target person from video sequences captured by a distributed camera system. It remains a challenging task due to the reasons such as occlusion and misalignment in the video. To address above problem, many methods are proposed to exploit multi-scale spatio-temporal features in videos. However, established methods typically assign the equal weights to temporal or spatial features at different scales, which significantly diminishes the distinct roles of each feature. In this paper, we propose a novel Multi-scale Feature Aggregation Network (MFANet) for video-based person Re-ID. Specifically, we propose two flexible modules, Multi-scale Temporal Feature Aggregation (MTFA) and Multi-scale Spatial Feature Aggregation (MSFA). These two modules first extract different scales of temporal (dynamic, static) and spatial (coarse and fine) features, and then adaptively assign weights to each feature according to the video sequence. Both of these lightweight modules can be incorporated with 3D Convolutional Neural Network to build our MFANet. Extensive experiments on four public benchmarks demonstrate that MTFA and MSFA improve the performance of baseline architectures, and our MFANet achieves the best performance compared to other state-of-the-art methods.

Enhancing Person Re-Identification through Attention-Driven Global Features and Angular Loss Optimization.

To address challenges related to the inadequate representation and inaccurate discrimination of pedestrian attributes, we propose a novel method for person re-identification, which leverages global feature learning and classification optimization. Specifically, this approach integrates a Normalization-based Channel Attention Module into the fundamental ResNet50 backbone, utilizing a scaling factor to prioritize and enhance key pedestrian feature information. Furthermore, dynamic activation functions are employed to adaptively modulate the parameters of ReLU based on the input convolutional feature maps, thereby bolstering the nonlinear expression capabilities of the network model. By incorporating Arcface loss into the cross-entropy loss, the supervised model is trained to learn pedestrian features that exhibit significant inter-class variance while maintaining tight intra-class coherence. The evaluation of the enhanced model on two popular datasets, Market1501 and DukeMTMC-ReID, reveals improvements in Rank-1 accuracy by 1.28% and 1.4%, respectively, along with corresponding gains in the mean average precision (mAP) of 1.93% and 1.84%. These findings indicate that the proposed model is capable of extracting more robust pedestrian features, enhancing feature discriminability, and ultimately achieving superior recognition accuracy.

A multitask tensor-based relation network for cloth-changing person re-identification

The Cloth-changing person re-identification (CC-ReID) is more challenging than person re-identification (ReID) because of the unreliability of cloth-relevant features. Existing CC-ReID methods output the unique latent vector feature for the same pedestrian image. However, on the one hand, the latent vector feature generated by the pooling layer will lose more spatial information. On the other hand, the features that they focus on are the same for the same pedestrian image under different comparison pedestrian images, which does not consider the pedestrian pair relation information. For this, we propose a Multitask Tensor-based Relation Network (MTTRN) for CC-ReID. In MTTRN, we utilize the augmented cloth-changing identity images, human parsing images and head images to guide the model learning more fine-grained cloth-irrelevant feature cues. We propose a novel channel subspace generator and use the tensor mode-n product to generate the subspace tensor features instead of the vector feature generated by the pooling layer, which can retain more spatial feature information. Furthermore, we assume that the model will focus on different features for the same pedestrian image under different comparison pedestrian images in the same feature subspace or the same pedestrian image pair in different feature subspaces, in which the tensor feature pair relation is considered fully to mine more robust features. Extensive experiments show that our method achieves the state-of-the-art or competitive performance on three CC-ReID benchmark datasets and demonstrate the robustness of our model.

Synthetic Data for Video Surveillance Applications of Computer Vision: A Review

In recent years, there has been a growing interest in synthetic data for several computer vision applications, such as automotive, detection and tracking, surveillance, medical image analysis and robotics. Early use of synthetic data was aimed at performing controlled experiments under the analysis by synthesis approach. Currently, synthetic data are mainly used for training computer vision models, especially deep learning ones, to address well-known issues of real data, such as manual annotation effort, data imbalance and bias, and privacy-related restrictions. In this work, we survey the use of synthetic training data focusing on applications related to video surveillance, whose relevance has rapidly increased in the past few years due to their connection to security: crowd counting, object and pedestrian detection and tracking, behaviour analysis, person re-identification and face recognition. Synthetic training data are even more interesting in this kind of application, to address further, specific issues arising, e.g., from typically unconstrained image or video acquisition conditions and cross-scene application scenarios. We categorise and discuss the existing methods for creating synthetic data, analyse the synthetic data sets proposed in the literature for each of the considered applications, and provide an overview of their effectiveness as training data. We finally discuss whether and to what extent the existing synthetic data sets mitigate the issues of real data, highlight existing open issues, and suggest future research directions in this field.

Pyramid-resolution person restoration for cross-resolution person re-identification

Pyramid-resolution person restoration for cross-resolution person re-identification

Deep video-based person re-identification (Deep Vid-ReID): comprehensive survey

Person re-identification (ReID) aims to find the person of interest across multiple non-overlapping cameras. It is considered an essential step for person tracking applications which is vital for surveillance. Person ReID could be investigated either using image-based or video-based. Video-based person ReID is considered more discriminating and realistic than image-based ReID due to the massive information extracted for each person. Different deep-learning techniques have been used for video-based ReID. In this survey, recently published articles are reviewed according to video-based ReID system pipeline: deep features learning, deep metric learning, and deep learning approaches. The deep feature learning approaches are categorized into spatial and temporal approaches, while deep metric learning is divided into metric and metric learning approaches. The deep learning approaches are differentiated into: supervised, unsupervised, weakly-supervised, and one-shot learning. A detailed analysis is held for the architectures of the state-of-the-art deep learning approaches. And their performance on four benchmark datasets is compared.

Domain generalization person re-identification via style adaptation learning

Domain generalization person re-identification via style adaptation learning

Multi deep invariant feature learning for cross-resolution person re-identification

Person re-identification (Re-ID) is focused on identifying and matching the same pedestrian when captured across various surveillance cameras. However, variations in camera performance and the distances between pedestrians and cameras may result in capturing images of the same person at different resolutions. This specific issue, referred to as cross-resolution person re-identification (CRReID), presents considerable difficulties in achieving accurate person Re-ID. To address this issue, we propose a Multi-level and scale Deep Invariant Feature learning Framework (MDIFF), which effectively tackles the problem of cross-resolution person matching. Our MDIFF reconstructs person features at the shallow layers to alleviate information gaps and extracts resolution-invariant features at the deep layers for cross-resolution person matching. First, to mitigate information loss in resolution-invariant features, we propose a Dual Input Feature Reconstruction (DIFR) structure incorporating dual-stream input and a lightweight decoder, constrained by degradation loss and image reconstruction loss. Second, we propose a Multi-level Global–Local feature Interaction and Fusion (MGLIF) module to enhance the invariant features of persons and obtain deep invariant representations, making the final representation more robust to resolution changes and more discriminative. Finally, to make the feature distribution of the same identity more compact across different resolutions, we propose a cross-resolution joint loss optimization strategy, including cross-resolution triplet loss, cross-resolution center loss, and identity loss. Our comprehensive experimental results demonstrate the superior performance and efficacy of our MDIFF, outperforming current state-of-the-art methods across various CRReID benchmark datasets. Our code is available at https://github.com/MiSanl/MDIFF-for-CRReID.

IGIE-net: Cross-modality person re-identification via intermediate modality image generation and discriminative information enhancement

Given an RGB image (or an IR image) of a pedestrian, the task of cross-modality person re-identification aims to retrieve images of a specific pedestrian from an IR (or RGB) gallery. However, the huge modality discrepancy between RGB and IR images significantly affects the performance of cross-modality person re-identification. For the purpose of reducing the impact of modality discrepancy and extracting more discriminative pedestrian features, a new cross-modality person re-identification network is proposed by us, which is based on intermediate modality image generation and discriminative information enhancement (IGIE-Net). Specifically, we design an intermediate modality image generation module (IMIGM) to effectively weaken the impact of modality discrepancy, which first extracts the modality-specific and modality-shared information contained in RGB and IR images separately, and then generates intermediate RGB and intermediate IR images by adaptively fusing original RGB and original IR images with their corresponding modality-specific and modality-shared information separately. In addition, we also design a discriminative information enhancement module (DIEM) to obtain more discriminative pedestrian representations by enhancing the discriminative information contained in the deep features of pedestrians. Extensive experiments on the publicly available SYSU-MM01 and RegDB datasets indicate that the performance of IGIE-Net reaches the current advanced level.

A Multi-Level Relation-Aware Transformer model for occluded person re-identification

Occluded person re-identification (Re-ID) is a challenging task, as pedestrians are often obstructed by various occlusions, such as non-pedestrian objects or non-target pedestrians. Previous methods have heavily relied on auxiliary models to obtain information in unoccluded regions, such as human pose estimation. However, these auxiliary models fall short in accounting for pedestrian occlusions, thereby leading to potential misrepresentations. In addition, some previous works learned feature representations from single images, ignoring the potential relations among samples. To address these issues, this paper introduces a Multi-Level Relation-Aware Transformer (MLRAT) model for occluded person Re-ID. This model mainly encompasses two novel modules: Patch-Level Relation-Aware (PLRA) and Sample-Level Relation-Aware (SLRA). PLRA learns fine-grained local features by modeling the structural relations between key patches, bypassing the dependency on auxiliary models. It adopts a model-free method to select key patches that have high semantic correlation with the final pedestrian representation. In particular, to alleviate the interference of occlusion, PLRA captures the structural relations among key patches via a two-layer Graph Convolution Network (GCN), effectively guiding the local feature fusion and learning. SLRA is designed to facilitate the model to learn discriminative features by modeling the relations among samples. Specifically, to mitigate noisy relations of irrelevant samples, we present a Relation-Aware Transformer (RAT) block to capture the relations among neighbors. Furthermore, to bridge the gap between training and testing phases, a self-distillation method is employed to transfer the sample-level relations captured by SLRA to the backbone. Extensive experiments are conducted on four occluded datasets, two partial datasets and two holistic datasets. The results show that the proposed MLRAT model significantly outperforms existing baselines on four occluded datasets, while maintains top performance on two partial datasets and two holistic datasets.