Person Re-identification Algorithm Research Articles

Multi-branch Collaboration Based Person Re-identification

As computer vision algorithms have advanced rapidly, the widespread deployment of video surveillance systems has enhanced traffic safety and propelled the growth of intelligent highway systems. However, the intricate nature of real-world scenarios, particularly the presence of occlusions, introduces noise that can cause the loss of critical feature information for identified individuals or objects. This poses significant challenges to current person re-identification algorithms. In response, this study introduces an innovative person re-identification approach that leverages a hybrid network architecture. The method performs feature extraction across four collaborative branches: a local branch, a global branch, a global contrast pooling branch, and an associative branch. This comprehensive approach yields a robust and diverse representation of person features, addressing the limitations posed by occlusion and noise in the environment. The neural network presented in this study is versatile and can be integrated with various backbone architectures. Empirical evaluations demonstrate that our proposed algorithm outperforms state-of-the-art techniques, while deep ablation studies substantiate the efficacy of the network’s design. This suggests that the architecture contributes significantly to the performance gains observed.

A cross-domain person re-identification algorithm based on distribution-consistency and multi-label collaborative learning

A cross-domain person re-identification algorithm based on distribution-consistency and multi-label collaborative learning

Construction Activity Recognition Method Based on Object Detection, Attention Orientation Estimation, and Person Re-Identification

Recognition and classification for construction activities help to monitor and manage construction workers. Deep learning and computer vision technologies have addressed many limitations of traditional manual methods in complex construction environments. However, distinguishing different workers and establishing a clear recognition logic remain challenging. To address these issues, we propose a novel construction activity recognition method that integrates multiple deep learning algorithms. To complete this research, we created three datasets: 727 images for construction entities, 2546 for posture and orientation estimation, and 5455 for worker re-identification. First, a YOLO v5-based model is trained for worker posture and orientation detection. A person re-identification algorithm is then introduced to distinguish workers by tracking their coordinates, body and head orientations, and postures over time, then estimating their attention direction. Additionally, a YOLO v5-based object detection model is developed to identify ten common construction entity objects. The worker’s activity is determined by combining their attentional orientation, positional information, and interaction with detected construction entities. Ten video clips are selected for testing, and a total of 745 instances of workers are detected, achieving an accuracy rate of 88.5%. With further refinement, this method shows promise for a broader application in construction activity recognition, enhancing site management efficiency.

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.

Person re-identification algorithm by image from video surveilance sistem using a neural network compound descriptor

For people re-identification in distributed video surveillance systems, it is important to have an algorithm that ensures efficiency when a person is blocked by other people or objects. Therefore, for this task, an algorithm has been developed that involves the compound descriptor formation, which includes a global features vector of a person’s image and three local ones for its upper, middle and down parts. The regions of interest selection is carried out based on the detecting key points results in the image of the human body. If a person's image part is occluded by other people or objects, then it is classified as invisible. The hidden part person image is not used in the formation of the local feature. To obtain it, the average value of the k-nearest neighbors such features of a person’s image is calculated. The experiments performed indicate an increase in re-identification accuracy for the Market-1501, DukeMTMC-ReID, MSMT17 and PolReID1077 datasets.

A domain generalized person re-identification algorithm based on meta-bond domain alignment☆

Domain Generalization (DG) model is an important tool to improve the robustness of person re-identification algorithm, but the domain gap makes it difficult to transfer knowledge cross-domain effectively. To solve the above problems, this paper proposes a generalization model based on a Meta-Bond Domain Alignment (M−BDA) model. To learn a generalizable model, a meta-learning strategy is introduced to simulate the training–testing process of the domain generalization. Then a bond-domain module is constructed in the training to align the source domain, which can reduce the domain gap between the source domain and the target domain, and facilitate the knowledge transfer. Finally, the bond-domain loss is counted on the feature space of the bond-domain to prevent the generated bond-domain from overfitting to the source domain. The experimental results show that the proposed algorithm achieves better performance in DukeMTMC-ReID, Market1501 and MSMT17 alternating as the source and the target domain tasks. In the generalization experiments of Market-1501 → DukeMTMC-ReID, mAP and Rank-1 increased by 7.7 % and 3.6 %, respectively, and in the generalization experiments of DukeMTMC-ReID → Market-1501, mAP and Rank-1 are increased by 9.3 % and 3.5 %, respectively, which are significantly better than the newer representative algorithms.

UnA-Mix: Rethinking Image Mixtures for Unsupervised Person Re-Identification

With the development of ultra-long-range visual sensors, the application of unsupervised person re-identification algorithms to them has become increasingly important. However, these algorithms inevitably generate noisy pseudo-labels, which seriously hinder the performance of tasks over a large range. Mixup, a data enhancement technique, has been validated in supervised learning for its generalization to noisy labels. Based on this observation, to our knowledge, this study is the first to explore the impact of the mixup technique on unsupervised person re-identification, which is a downstream task of contrastive learning, in detail. Specifically, mixup was applied in different locations (at the pixel level and feature level) in an unsupervised person re-identification framework to explore its influences on task performance. In addition, based on the richness of the information contained in the person samples to be mixed, we propose an uncertainty-aware mixup (UnA-Mix) method, which reduces the over-learning of simple person samples and avoids the information damage that occurs when information-rich person samples are mixed. The experimental results on three benchmark person re-identification datasets demonstrated the applicability of the proposed method, especially on the MSMT17, where it outperformed state-of-the-art methods by 5.2% and 4.8% in terms of the mAP and rank-1, respectively.

Person re-identification algorithm based on identity information matrix

Person re-identification algorithm based on identity information matrix

Multi-level Salient Feature Mining Network for Person Re-identification

Person re-identification (Re-ID) algorithms can retrieve the same pedestrian’s images from an image gallery captured by multiple cameras when given a pedestrian image. Due to changes in pedestrian postures, illuminations, and perspectives, it remains a significant challenge to improve the accuracy of person re-identification. Although the attention mechanism can alleviate some of these issues, it causes attention-based methods to pay excessive attention to features in the most salient areas of images while ignoring discriminant features outside the most salient areas, resulting in the insufficient discriminability of features extracted by attention-based methods. For this purpose, we propose a Multi-level Salient Feature Mining Network (MSFM-Net). First, by embedding attention modules in ResNet50, the model extract the most salient pedestrian feature maps. Second, the model uses two sub-salient feature mining branches to extract the second-level and third-level salient feature maps (collectively referred to as sub-salient feature maps). Third, the model uses the feature maps fusion module to combine the most salient feature maps with sub-salient feature maps to obtain the fused salient feature maps. Finally, the model pools the fused salient feature maps to produce more discriminant pedestrian representations. The results of two benchmark datasets demonstrate that MSFM-Nets performance reaches the current advanced level.

Multi-Camera Person Re-Identification Based on Trajectory Data

This study presents a trajectory-based person re-identification algorithm, embedded in a tool to detect and track customers present in a large retail store, in a multi-camera environment. The customer trajectory data are obtained from video surveillance images captured by multiple cameras, and customers are detected and tracked along the frames that compose the videos. Due to the characteristics of a multi-camera environment or the occurrence of occlusions, caused by objects such as shelves or counters, different identifiers are assigned to each person when, in fact, they should be identified with a unique identifier. Thus, the proposed tool tries to solve this problem in a scenario where there are constraints in using images of people due to data privacy concerns. The results show that our method was able to correctly re-identify the customers present in the store with a re-identification rate of 82%.

Weakly Supervised Cross-Domain Person Re-Identification Algorithm Based on Small Sample Learning

This paper proposes a weakly supervised cross-domain person re-identification (Re-ID) method based on small sample data. In order to reduce the cost of data collection and annotation, the model design focuses on extracting and abstracting the information contained in the data under limited conditions. In this paper, we focus on the problems of strong data dependence, weak cross-domain capability and low accuracy in Re-ID in weakly supervised scenarios. Our contributions are as follows: first, we implement a joint training framework with the help of small sample learning and cross-domain migration for Re-ID. Second, with the help of residual compensation and fusion attention module, the RCFA module is designed, and the model framework is built on this basis to improve the cross-domain ability of the model. Third, to solve the problem of low accuracy caused by insufficient data coverage of small samples, a fusion of shallow features and deep features is designed to enable the model to weighted fusion of shallow detail information and deep semantic information. Finally, by selecting different camera images in Market1501 dataset and DukeMTMC-reID dataset as small samples, respectively, and introducing another dataset data for joint training, we demonstrate the feasibility of this joint training framework, which can perform weakly supervised cross-domain Re-ID based on small sample data.

Cross-Modality Person Re-Identification Algorithm Based on Two-Branch Network

Person re-identification is the technique of identifying the same person in different camera shots, known as ReID for short. Most existing models focus on single-modality person re-identification involving only visible images. However, the visible modality is not suitable for low-light environments or at night, when crime is frequent. In contrast, infrared images can reflect the nighttime environment, and most surveillance systems are equipped with dual-mode cameras that can automatically switch between visible and infrared modalities based on light conditions. In contrast to visible-light cameras, infrared (IR) cameras can still capture enough information from the scene in those dark environments. Therefore, the problem of visible-infrared cross-modality person re-identification (VI-ReID) is proposed. To improve the identification rate of cross-modality person re-identification, a cross-modality person re-identification method based on a two-branch network is proposed. Firstly, we use infrared image colorization technology to convert infrared images into color images to reduce the differences between modalities and propose a visible-infrared cross-modality person re-identification algorithm based on Two-Branch Network with Double Constraints (VI-TBNDC), which consists of two main components: a two-branch network for feature extraction and a double-constrained identity loss for feature learning. The two-branch network extracts the features of both data sets separately, and the double-constrained identity loss ensures that the learned feature representations are discriminative enough to distinguish different people from two different patterns. The effectiveness of the proposed method is verified by extensive experimental analysis, and the method achieves good recognition accuracy on the visible-infrared image person re-identification standard dataset SYSU-MM01.

AEA-Net:Affinity-supervised entanglement attentive network for person re-identification

Most existing person re-identification algorithms prioritize the extraction of effective and salient local features while neglecting the affinity between local and adjacent features. This phenomenon will cause misidentification when different people have similar local features. To address this issue, we introduce the AEA-Net, which emphasizes the affinity between the local features of a single image. Specifically, three important components are proposed. The affinity-supervised attention module (ASA) centers on adjacent features and utilizes the affinity between global and adjacent features to supervise the learning of attention. The affinity relationship module (AR) focuses on constructing relationship features between local and adjacent features to enhance the closeness between local features. The tangle hybrid loss (THL) makes the final predictions have a distinct weight profile. Extensive experiments quantitatively and qualitatively demonstrate that our method outperforms the state-of-the-art approaches.

Dual-Branch Person Re-Identification Algorithm Based on Multi-Feature Representation

Person re-identification can identify specific pedestrians across cameras and solve the visual limitations of a single fixed camera scene. It achieves trajectory analysis of target pedestrians, facilitating case analysis by public security personnel. Person re-identification has become a challenging problem due to occlusion, blur, posture change, etc. The key to affecting the accuracy is whether sufficient information of pedestrians can be obtained. Most existing deep learning methods only consider a single category of characteristics, resulting in problems of information loss and single feature expression. This paper proposed a dual-branch person re-identification algorithm based on multi-scale and multi-granularity branches to obtain complete pedestrian information, called MSMG-Net. The multi-scale branch extracted features from different layers and fused them through the bidirectional cross-pyramid structure. It made up for the loss of information caused by using only high-level semantic features. The multi-granularity branch combined the convolutional neural network (CNN) and Transformer module based on improved self-attention mechanisms to obtain global information, and it performed horizontal segmentation of feature maps to obtain local information. It combined global and local features and solved the problem of single feature expression. The experimental results show that mAP/Rank-1 of MSMG-Net reached 88.6%/96.3% on the Market-1501 dataset and 80.1%/89.9% on the DukeMTMC-ReID dataset. Compared with many state-of-the-art methods, the performance of the proposed algorithm was improved significantly.

Semi-supervised LDA pedestrian re-identification algorithm based on K-nearest neighbor resampling

Person re-identification identify a specific person in surveillance network by similarity measurement between images of different camera views. However, existing metric learning based methods suffer from over-fitting problem. To solve this problem, a resampled linear discriminant analysis (LDA) method was proposed based on the statistical and topological characteristics of pedestrian images. This method utilized the k-nearest neighbours to form potential positive sample pairs. The potential positive pairs are used to improve the metric model and generalize the metric model to the test data. By minimizing the inter-class divergence of potential positive sample pairs, a semi-supervised re-sampling LDA person re-identification algorithm was established. It was then tested on the VIPeR, CUHK01 and Market 1501datasets. The results show that the proposed method achieves the best performance compared to some available methods. Especially, the proposed method outplays the best comparison method by 0.6% and 5.76% at rank-1 identification rate on the VIPeR and CUHK01 datasets respectively. At the same time, the improved LDA algorithm has improved the rank-1 identification accuracy of traditional LDA method by 9.36% and 32.11% on these two datasets respectively. Besides, the proposed method is limited to Market-1501 dataset when the test data is of large size.

An Image–Text Dual-Channel Union Network for Person Re-Identification

Multiple people can have similar appearances and portions of images can be occluded or have viewpoint changes in real scenarios, causing the increased difficulty of person re-identification (Re-ID). To address these problems, we propose a dual-channel person Re-ID algorithm that integrates person Re-ID image-text pairs into the classification network for end-to-end learning. We construct an image channel and a text channel, and subsequently extract visual information and text information using a convolutional neural network and a simple recurrent units network, respectively. The text information is used to assist in the learning of visual information, consequently improving the robustness of the visual information. In addition, the visual features are divided into two branches to calculate the global and local features. Global features focus on the overall appearance of a person, whereas local features provide more fine-grained detail. Text information is more accurate and reliable, and it is thus more robust to occlusion and viewpoint changes. Visual information complemented by text information can describe a person more accurately and reliably. Extensive experiments demonstrate our method achieves state-of-the-art performance.

Learning from self-discrepancy via multiple co-teaching for cross-domain person re-identification

Employing clustering strategy to assign unlabeled target images with pseudo labels has become a trend for person re-identification (re-ID) algorithms in domain adaptation. A potential limitation of these clustering-based methods is that they always tend to introduce noisy labels, which will undoubtedly hamper the performance of our re-ID system. To handle this limitation, an intuitive solution is to utilize collaborative training to purify the pseudo label quality. However, there exists a challenge that the complementarity of two networks, which inevitably share a high similarity, becomes weakened gradually as training process goes on; worse still, these approaches typically ignore to consider the self-discrepancy of intra-class relations. To address this issue, in this paper, we propose a multiple co-teaching framework for domain adaptive person re-ID, opening up a promising direction about self-discrepancy problem under unsupervised condition. On top of that, a mean-teaching mechanism is leveraged to enlarge the difference and discover more complementary features in target domain. Comprehensive experiments conducted on several large-scale datasets show that our method achieves competitive performance compared with the state-of-the-arts.

Attribute-Guided Global and Part-Level Identity Network for Person Re-Identification

Most of the person re-identification (re-ID) algorithms based on deep learning mainly learn the global feature representation of pedestrians, while ignoring the important role of fine-grained pedestrian attribute features on re-ID tasks. Pedestrian attributes are middle-level semantic features, which have invariance in different poses, camera views, and illumination conditions. Considering the robustness and promotion of pedestrian attributes for person re-ID task, we propose an Attribute-guided Global and Part-level identity Network (AGPNet), which consists of a global identity task, a part-level identity task, and a pedestrian attributes learning task. AGPNet takes advantage of perceived semantic information of pedestrian attributes and deploys them as guidance to attend to human body regions and learn robust feature representation in the feature representation construction stage. Extensive experiments on two large-scale person re-ID datasets (Market-1501 and DukeMTMC-reID) show the effectiveness of our method, which is competitive with the state-of-the-art algorithms.

A novel unsupervised person re-identification algorithm based on soft multi-label and compound attention model

A novel unsupervised person re-identification algorithm based on soft multi-label and compound attention model

Evolution of ICTs-empowered-identification: A general re-ranking method for person re-identification

Re-ranking is becoming a critical part of retrieved based Person re-identification algorithms. Existing re-ranking methods always require lots of queries and memory to go through the k -nearest neighbors. To solve this problem, we introduce a Feature Relation Map (FRM) to mine the latent relation between the k -neighbors through convolution neural network, and propose a metric learning based Similarity Evaluation (SE) model to obtain the re-ranking distance from the FRM. The dilated convolution is then introduced by concatenating the dilated convolution kernels and normal convolution kernels along the channel dimension in the improvement of the SE model (named SE-d model), to allow the SE model to efficiently compare more samples pairs for obtaining the final re-ranking distance. Further, we embedding out FRM-SE model to the existing re-ranking methods to prove the effectiveness of our re-ranking model. The experiments on Market1501, CUHK03, DukeMTMC and MSMT17 benchmarks illustrates the superiority of proposed method to the state-of-the-art re-ranking methods. Although the SE model make a great improve performance, the SE-d model gains an increase of 0.31%, 0.63% in top-1 respectably compared with the SE model in the DukeMTMC and MSMT17 datasets. Meanwhile, the SE-d model speed up the model convergence and shorten the training time. Furthermore, in the transfer learning setting, the model trained on either Market1501, CUHK03 or DukeMTMC can achieve a comparable accuracy improvement on the MSMT17 dataset, which validates the generalization of our SE model.