Advancements in Person Re-Identification Through Artificial Intelligence Techniques

Robust ensemble person reidentification via orthogonal fusion with occlusion handling

Source-free Style-diversity Adversarial Domain Adaptation with Privacy-preservation for person re-identification

State-of-the-art person re-identification (ReID) models use Convolutional Neural Networks (CNN) for feature extraction and comparison. Often these models fail to recognize all the intra- and inter-class variations that emerge in person ReID, making it harder to discriminate between data subjects. In this paper we seek to reduce these problems and improve performance by combining two state-of-the-art models. We use the Omni-Scale Network (OSNet) as our CNN to test the Market1501 and DukeMTMC-ReID datasets for person ReID. To fully utilize the potential of these datasets, we apply the spatialtemporal constraint which extracts the camera ID and timestamp from each image to form a distribution. We combine these two methods to create a hybrid model titled Spatial-Temporal OmniScale Network (st-OSNet). Our model attains a Rank-1 (R1) accuracy of 98.2% and mean average precision (mAP) of 92.7% for the Market1501 dataset. For the DukeMTMC-reID dataset our model achieves 94.3% R1 and 86.1% mAP, hereby surpassing the results of OSNet by a large margin for both datasets (94.3%, 86.4%, 88.4%, 76.1%, respectively).

Person re-identification (Re-ID) is a critical task in video surveillance and security systems, aiming to match individuals across non-overlapping camera views. However, significant challenges arise due to variations in illumination, pose, and viewpoint. In this work, we propose a distance fusion-based approach to improve person Re-ID accuracy by effectively combining multiple feature-based distance metrics. We extract discriminative features using Local Maximal Occurrence (LOMO), Gaussian of Gaussian (GOG), and Convolutional Neural Networks (CNN). The extracted features are projected into a lower-dimensional space using Cross-view Quadratic Discriminant Analysis (XQDA), and their distances are computed and normalized using Min-Max scaling. To enhance performance, we implement two fusion strategies: Distance Fusion by Simple Sum (DFSS), an unsupervised method that aggregates normalized distances, and Distance Fusion by Logistic Regression (DFLR), a supervised approach that learns an optimal combination of distances. The proposed techniques are evaluated on four benchmark datasets: VIPeR, PRID450S, GRID, and CUHK01. Experimental results demonstrate that our fusion-based methods significantly outperform individual feature-based approaches, achieving state-of-the-art accuracy. Specifically, the fusion of CNN, LOMO, and GOG in the DFLR framework achieves the best performance across all datasets, with improvements in Rank-1 and Rank-20 accuracy. These findings highlight the effectiveness of distance fusion in improving person Re-ID performance, making it a promising approach for real-world applications.

Two-phase self-supervised pretraining for object re-identification

Person re-identification (Re-ID) aims to match pedestrian images across various scenes in video surveillance. There are a few works using attribute information to boost Re-ID performance. Specifically, those methods leverage attribute information to boost Re-ID performance by introducing auxiliary tasks like verifying the image level attribute information of two pedestrian images or recognizing identity level attributes. Identity level attribute annotations cost less manpower and are well-fitted for person re-identification task compared with image-level attribute annotations. However, the identity attribute information may be very noisy due to incorrect attribute annotation or lack of discriminativeness to distinguish different persons, which is probably unhelpful for the Re-ID task. In this paper, we propose a novel Attribute Attentional Block (AAB), which can be integrated into any backbone network or framework. Our AAB adopts reinforcement learning to drop noisy attributes based on our designed reward and then utilizes aggregated attribute attention of the remaining attributes to facilitate the Re-ID task. Experimental results demonstrate that our proposed method achieves state-of-the-art results on three benchmark datasets.

Various challenges exist throughout person re-identification (ReID) process, including background clutters, illumination variation, pose variation, occlusion, etc. Addressing these problems, this paper explores the incorporation of human attention mechanism in person ReID and proposes an attention-aware model named Body-guided Attention Network (BANet). The proposed attention is based on the body masked images which are obtained by a reliable pixel-level segmentation strategy. To optimize the feature representation learning so as to pay more attention to the discriminative details of human body, BANet is built. It is composed of three attention branches. In order to guide attention learning layer by layer, these branches are applied to the convolution features of different levels. The proposed BANet aims to fully utilize fine-grained information of body region to guide the final process of feature extraction. Extensive experiments on benchmarks including CUHK03, Market1501 and DukeMTMC-reID show that BANet can achieve state-of-the-art performance, which validates the importance of attention mechanism in person ReID.

Driven by growing public security demands and the advancement of intelligent surveillance systems, person re-identification (ReID) has emerged as a prominent research focus in the field of computer vision. However, this task presents challenges due to its high sensitivity to variations in visual appearance caused by factors such as body pose and camera parameters. Although deep learning-based methods have achieved marked progress in ReID, the high cost of annotation remains a challenge that cannot be overlooked. To address this, we propose an unsupervised attribute learning framework that eliminates the need for costly manual annotations while maintaining high accuracy. The framework learns the mid-level human attributes (such as clothing type and gender) that are robust to substantial visual appearance variations and can hence boost the accuracy of attributes with a small amount of labeled data. To carry out our framework, we present a part-based convolutional neural network (CNN) architecture, which consists of two components for image and body attribute learning on a global level and upper- and lower-body image and attribute learning at a local level. The proposed architecture is trained to learn attribute-semantic and identity-discriminative feature representations simultaneously. For model learning, we first train our part-based network using a supervised approach on a labeled attribute dataset. Then, we apply an unsupervised clustering method to assign pseudo-labels to unlabeled images in a target dataset using our trained network. To improve feature compatibility, we introduce an attribute consistency scheme for unsupervised domain adaptation on this unlabeled target data. During training on the target dataset, we alternately perform three steps: extracting features with the updated model, assigning pseudo-labels to unlabeled images, and fine-tuning the model. Through a unified framework that fuses complementary attribute-label and identity label information, our approach achieves considerable improvements of 10.6% and 3.91% mAP on Market-1501→DukeMTMC-ReID and DukeMTMC-ReID→Market-1501 unsupervised domain adaptation tasks, respectively.

Person re-identification (Re-ID) aims to match pedestrian images cross multiple cameras. Most Re-ID studies focus on visible pedestrian images, without considering the images obtained by infrared cameras in the dark. To solve the cross-modality person Re-ID problem, current methods usually exploit global feature descriptors to obtain discriminative representations. However, they ignore the fine-grained information of heterogeneous images. In this paper, we propose an adaptive body partition (ABP) model to automatically detect and distinguish effective part representations. Instead of utilizing a two-stream convolutional neural network (CNN) to extract modality-specific information, we directly design an end-to-end one-stream CNN to simultaneously learn multi-modality sharable features and map them on a common space. Global loss, part losses and threefold triplet loss are integrated to enhance the feature discriminability and minimize the cross-modality gap. Extensive experimental results on two cross-modality Re-ID datasets exhibit the superiority of the proposed method compared with the state-of-the-art solutions.

A review on video person re-identification based on deep learning

Extracting robust feature representation is one of the key challenges for person re‐identification (ReID) task. Although convolution neural network (CNN)‐based methods have achieved great success, they still cannot handle the part occlusion and misalignment caused by limited receptive field. Recently, pure transformer models have shown its power in the person ReID task. However, current transformer models adopt patches of equal‐scale as input, and cannot solve the problem of cross‐scale interaction properly. To overcome this problem, an adaptive cross‐scale transformer from a perspective of the graph signal, named ACSFormer, is proposed. Specifically, the self‐attention module is first treated as an undirected fully connected graph. And then, “node variation” is introduced as an indicator to adaptively merge neighbourhood tokens. To the best of the authors’ knowledge, their ACSFormer is the first work to attempt to combine pure transformers and graph signal processing in the field of person ReID. Extensive evaluations are conducted on three person ReID datasets to validate the performance of ACSFormer. Experiments demonstrate that this ACSFormer performs on par with state‐of‐the‐art CNN‐based methods and consistently improves transformer‐based baseline, for example, surpassing ViT‐baseline by 2.5%, 2.7% and 4.8% mAP on Market1501, DukeMTMC‐reID and MSMT17, respectively.

Domain adaption (DA) for person re-identification (ReID) has attained considerable progress by transferring knowledge from a source domain with labels to a target domain without labels. Nonetheless, most of the existing methods require access to source data, which raises privacy concerns. Source-free DA has recently emerged as a response to these privacy challenges, yet its direct application to open-set pedestrian re-identification tasks is hindered by the reliance on a shared category space in existing methods. Current source-free DA approaches for person ReID still encounter several obstacles, particularly the divergence-agnostic problem and the notable domain divergence due to the absent source data. In this article, we introduce an Instance-level Adversarial Mutual Teaching (IAMT) framework, which utilizes adversarial views to tackle the challenges mentioned above. Technically, we first elaborately develop a variance-based division (VBD) module to segregate the target data into instance-level subsets based on their similarity and dissimilarity to the source using the source-trained model, implicitly tackling the divergence-agnostic problem. To mitigate domain divergence, we additionally introduce a dynamic adversarial alignment (DAA) strategy, aiming to enhance the consistence of feature distribution across domains by employing adversarial instances from the target data to confuse the discriminators. Experiments reveal the superiority of the IAMT over state-of-the-art methods for DA person ReID tasks, while preserving the privacy of the source data.

Abstract. Person Re-Identification (Re-ID) is the task of matching the same person in multiple images captured by different cameras. Recently deep learning-based Re-ID algorithms demonstrated an exciting progress for terrestrial-based cameras, still, person Re-ID in aerial images poses multiple challenges including occlusion of human feature parts, image distortion, and dynamic camera location. In this paper, we propose a new Person Aerial Re-ID framework Robust to Occlusion and Thermal imagery (ParrotGAN). Our model is focused on cross-modality person Re-ID in aerial images. Furthermore, we collected a new large-scale synthetic multimodal AerialReID dataset with 30k images and 137 person ID. Our ParrotGAN model leverages two strategies to achieve robust performance in the task of person Re-ID in thermal and visible range. Firstly, we use a latent space of StyleGAN2 model to estimate the distance between two images of a person. Specifically, we project each real image into the latent space with a correspondent latent vector z. We use the distance between latent vectors to provide a Re-ID similarity metric. Secondly, we use a generative-adversarial network to translate a color image to a synthetic thermal image. We use synthetic image for a cross-modality Re-ID. We evaluate our ParrotGAN model and basleines on our AerialReID and PRAI-1581 datasets. The results of the evaluation are encouraging and demonstrate that our ParrotGAN model competes with baselines in visible range aerial person Re-ID and outperforms them in the cross-modality setting. We made our code and dataset publicly available.

Person re-identification (ReID) is an important and practical task which identifies pedestrians across non-overlapping surveillance cameras based on their visual features. In general, ReID is an extremely challenging task due to complex background clutters, large pose variations and severe occlusions. To improve its performance, a robust and discriminative feature extraction methodology is particularly crucial. Recently, the feature alignment technique driven by human pose estimation, that is, matching two person images with their corresponding parts, increases the effectiveness of ReID to a certain extent. However, we argue that there are still a few problems among these methods such as imprecise handcrafted segmentation of body parts, and some improvements can be further achieved. In this paper, we present a novel framework called Mask-Guided Region Attention Network (MGRAN) for person ReID. MGRAN consists of two major components: Mask-guided Region Attention (MRA) and Multi-feature Alignment (MA). MRA aims to generate spatial attention masks and meanwhile mask out the background clutters and occlusions. Moreover, the generated masks are utilized for region-level feature alignment in the MA module. We then evaluate the proposed method on three public datasets, including Market-1501, DukeMTMC-reID and CUHK03. Extensive experiments with ablation analysis show the effectiveness of this method.

Fully-automated person re-identification in multi-camera surveillance system with a robust kernel descriptor and effective shadow removal method