Saliency-Weighted Global-Local Fusion for Person Re-identification

Person re-identification is an important task of matching pedestrians across non-overlapping camera views. In this paper, we exploit a weighted feature descriptor for person re-identification. We firstly compute the weights on the superpixel level via graph-based manifold ranking algorithm, then integrate the computed weights into a patch-based feature descriptor, named local maximal occurrence. Finally, the weighted descriptors are fed into a top-push distance learning to mitigate the cross-view gaps. We evaluate the proposed method on three benchmark datasets iLIDS-VID, PRID 450S and VIPeR. The promising experimental results demonstrate the effectiveness of the proposed method comparing with the state-of-the-arts.

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.

Given images of a person, person re- identification (Person ReID) techniques aim to find images of the same person from previously collected images. Because of large data sets of person images and the advance of deep learning, convolutional neural networks (CNNs) successfully boost the accuracy of Person ReID algorithms, but it can be difficult to explain and to troubleshoot issues due to the complexity of CNNs. In this paper, we present a visualization-based approach to understand a CNN-based Person ReID algorithm. As Person ReID algorithms are often designed to map images of the same person into similar feature vectors, given two images, we design an algorithm to estimate how much each element in a CNN layer contributes to the similarity between their feature vectors. Based on the estimation, we build a visualization tool to interactively locate and visualize the activation of highly-contributing elements, other than manually examining all. Our visualization tool also supports various user interaction widgets to explore a Person ReID data set, locate difficult cases, and analyze the reason behind their similarities. We show a use case with our tool to understand and troubleshoot issues in a CNN-based Person ReID algorithm.

Person re-identification, which aims to compare a person of interest as seen in a “probe” camera view to a “gallery” of candidates captured from a camera that does not overlap with the probe one, has increased significant attention in computer vision due to its application in surveillance and security. Various methods utilize the global information as the feature descriptors, which neglect the details in an image and may have a low accuracy of person re-identification and not much attention has been paid to suppressing the background information, which has an influence on person re-identification to some extent. Being aware of these problems, this paper concentrate on the appearance of a person by improving feature descriptors that shed light on a combination framework by fusing the attribute-identity discrimination network with the person discrimination network based on the visual attention mechanism. Experimental results on publicly available image benchmark data sets have demonstrated that the proposed combination framework can achieve competitive performances as compared with state-of-the-art algorithms in terms of accuracy and effectiveness.

Person re-identification (re-id) is a fundamental technique to associate various person images, captured by differentsurveillance cameras, to the same person. Compared to the single image based person re-id methods, video-based personre-id has attracted widespread attentions because extra space-time information and more appearance cues that can beused to greatly improve the matching performance. However, most existing video-based person re-id methods equally treatall video frames, ignoring their quality discrepancy caused by object occlusion and motions, which is a common phenomenonin real surveillance scenario. Based on this finding, we propose a novel video-based person re-id method via self paced weighting (SPW). Firstly, we propose a self paced outlier detection method to evaluate the noise degree of video sub sequences. Thereafter, a weighted multi-pair distance metric learning approach is adopted to measure the distance of two person image sequences. Experimental results on two public datasets demonstrate the superiority of the proposed method over current state-of-the-art work.

Traditional person re-identification methods are mostly based on supervised state of manually annotated data sets. Although the recognition accuracy is high, the algorithm completely relies on effective annotation information and lacks practicability. Based on the above problems, an unsupervised person re-identification method is proposed, which refines features to guide multi-label distribution sorting learning. Firstly, a single and multi-label classification network structure is constructed to improve the matching accuracy of tags by learning the characteristics of each person and its k nearest neighbor and sharing the same class. Secondly, the refinement feature extraction module is designed and embedded into the original ResNet50 network, and the potential key areas in the person image are adaptively located. While weakening the appearance differences of the features, multi-information mining is carried out to obtain the refined features of the characters and assist the classification network to improve the performance. Thirdly, reliable multi-category labels are screened by punishing the sorting between positive and negative categories of labels in multi-category labels. Finally, the network is supervised by combining multi-label distribution sorting loss and multi-label classification loss. Experiments show that without any annotation information, the recognition accuracy of Market-1501 and DukeMTMC-reID datasets is 58.4% and 42.3%, respectively, and the mAP of Market-1501 dataset is increased by 12.9%.

Deep and low-level feature based attribute learning for person re-identification

We address the problem of person re-identification (reID), that is, retrieving person images from a large dataset, given a query image of the person of interest. A key challenge is to learn person representations robust to intra-class variations, as different persons could have the same attribute, and persons' appearances look different, e.g., with viewpoint changes. Recent reID methods focus on learning person features discriminative only for a particular factor of variations (e.g., human pose), which also requires corresponding supervisory signals (e.g., pose annotations). To tackle this problem, we propose to factorize person images into identity-related and -unrelated features. Identity-related features contain information useful for specifying a particular person (e.g., clothing), while identity-unrelated ones hold other factors (e.g., human pose). To this end, we propose a new generative adversarial network, dubbed identity shuffle GAN (IS-GAN). It disentangles identity-related and -unrelated features from person images through an identity-shuffling technique that exploits identification labels alone without any auxiliary supervisory signals. We restrict the distribution of identity-unrelated features, or encourage the identity-related and -unrelated features to be uncorrelated, facilitating the disentanglement process. Experimental results validate the effectiveness of IS-GAN, showing state-of-the-art performance on standard reID benchmarks, including Market-1501, CUHK03 and DukeMTMC-reID. We further demonstrate the advantages of disentangling person representations on a long-term reID task, setting a new state of the art on a Celeb-reID dataset. Our code and models are available online: https://cvlab-yonsei.github.io/projects/ISGAN/.

Deep learning-based person re-identification methods: A survey and outlook of recent works

Person re-identification (Re-ID) aims to retrieve a person of interest across multiple nonoverlapping cameras. In the last few years, the construction of aerial person Re-ID datasets has been appealing because visual surveillance through unmanned aerial vehicle (UAV) platforms has become very valuable in real-world scenarios. However, great differences are exhibited between the pedestrian images captured by ground cameras and those captured by UAVs. Person Re-ID methods based on ground person images have difficulty when performing Re-ID based on aerial person images. In this paper, we first propose a novel meta-transfer learning method for person Re-ID in aerial imagery; this approach trains a generalisable Re-ID model to learn discriminative feature representations for aerial person images. Specifically, a meta-learning strategy is introduced to study a feature extractor, and a transfer learning strategy is introduced to utilise and further improve the acquired meta-knowledge. To overcome the convergence speed and recognition accuracy reductions caused by the presence of difficult categories in the given dataset, we propose a learning strategy based on curriculum sampling that is harmonised with our meta-transfer learning framework. In addition, a new metric formulation of sample similarity is introduced based on Mahalanobis distance to improve the optimisation of the model. Extensive comparative evaluation experiments are conducted on the large-scale aerial Re-ID dataset, and the results obtained show that our method achieves a Rank-1 accuracy of 63.63 $$\%$$ and a mean average precision (mAP) of 38.02 $$\%$$ , demonstrating its potential for completing person Re-ID in aerial images. Ablation studies also validate that each component contributes to improving the performance of the model.

This paper proposes a Multi-View based Pose Alignment (MVPA) method for person re-identification (re-id). Most recent methods solve re-id as a matching process based on single image. However, when poses vary or viewpoints change, the performance seriously deteriorates. This paper aims to learn a representation insensitive to view and pose. Specifically, we establish a set of Multi-view based Person Pose Templates (MPPT) and propose a Pose-Guided Person image Generation (iPG2) model to synthesize multi-view and uniform-pose based images. The representation learned from multi-view images can significantly enhances the accuracy of re-id. We evaluate our method on two popular datasets, i.e., Market-1501 and DukeMTMC-reID. The results show that our framework promotes the performance of re-id a lot and surpass other methods.KeywordsPerson re-identificationPose AlignmentGenerative Adversarial Networks

To improve the accuracy of person re-identification and reduce the size of models, a lightweight and accurate (LWA) unsupervised method is proposed. Firstly, a lightweight image classifier (LWC) is proposed. It combines DenseNet's convolutional layer and batch normalization layer to simplify its model structure. Then the spatio-temporal information of datasets is added to form a powerful classifier. Secondly, an optimization learning algorithm is proposed. A feeble classifier (LWB) is implemented by constructing a binary network. For the training of the network, the ranking information of a powerful classifier is used to enhance the recognition capability of the LWB. Furthermore, spatio-temporal information of datasets is added again to construct LWA classifier. The results show that the model size of LWB is about 80% smaller than that of the Siamese network model on the CUHK01 and VIPeR datasets. At the same time, the recognition accuracy of the LWA classifier is slightly improved on the Market-1501 and DukeMTMC target datasets.

Joint graph regularized dictionary learning and sparse ranking for multi-modal multi-shot person re-identification

Person Re-IDentification (Re-ID) is a critical technology to identify a target person from captured person images by surveillance cameras. However, person Re-ID has triggered great concerns of personal image privacy. Although the law (e.g., GDPR) has stipulated person images are personal private data, there is no an efficient solution to tackle the image privacy concern for person Re-ID. To this end, we propose FREED, the first system solution for privacy-preserving person Re-ID, which supports the state-of-the-art person Re-ID operations on encrypted feature vectors of person images. To handle the encryption of feature vectors effectively and enable person Re-ID operations on encrypted feature vectors efficiently, FREED develops a suite of batch secure computing protocols based on a twin-server architecture and the threshold Paillier cryptosystem. We demonstrate our secure computing protocols are more efficient than existing protocols and FREED achieves a precision equal to the state-of-the-art plaintext method.

Person re-identification is one of the hot topics in computer vision. How to design a robust feature representation to identify pedestrians is a key problem for person re-identification. In this paper, a feature representation based on Multi-Statistics Cascade on Pyramid (MSCP) is proposed for person re-identification. The MSCP feature is composed of deep PCA network feature and hand-crafted features of Local Maximal Occurrence (LOMO) feature and color correlogram. MSCP can characterize the pedestrian images precisely from both global and local views. The Cross-view Quadratic Discriminant Analysis (XQDA) is employed to learn the distance metric of MSCP features. And then a novel re-identification method based on MSCP and XQDA is achieved. Experimental results on VIPeR Dataset demonstrate that our proposed method can achieve superior identification performance compared with six state-of-art methods.