An Adaptive Cross-Resolution Network for Low-Resolution Person Re-Identification

Person re-identification(ReID) is a task of associating persons that cross the non-overlapping camera views at different locations and times. It is a challenging task due to the large variations in person pose, background, luminance, occlusion, low resolution, etc. How to extracting a powerful features representation is the prime problem in ReID and is still unsolved. In this paper, we propose a cascade network architecture combined with a generative adversarial networks(GANs) and a convolutional neural network(CNN) to improve the performance of person re-identification. The GANs first generates the person body parts segmentation from the person image, and then inputs the segmentation label into the connected CNN together with the original person image. Finally obtain a discriminative and robust feature representation for ReID task. The body parts segmentation partitioning the person image into multiple segments, such as background, head, face, arms, lags, etc. The body parts segmentation information contains accurate borders and category attributes for body parts, which makes the our model more accurate compared to other predefined rigid parts alignment models. Experiments are conduced on the CUHK03, Market1501, DukeMTMC-ReID datasets and the results demonstrate that this approach outperforms several existing state-of-the-art methods.

Generative adversarial network (GAN) has been widely applied to produce super-resolution (SR) image with real perception and texture details. In most existing SR approaches, the training objective typically measures a pixel-wise average distance between the SR and high-resolution (HR) images. However, as the degradation function of different images from HR to low resolution (LR) is generally different, optimizing such metrics often leads to certain unpleasant artificial traces. Unlike the prevalent GAN inversion methods that require expensive image-specific optimization at runtime, we present an alternative formulation by directly leveraging latent representation produced by a pretrained AutoEncoder. We call this improved method reduce dimension super-resolved GAN (RD-SRGAN). RD-SRGAN first obtains the latent feature representation of LR image by a pretrained AutoEncoder as input to the generator network. This process not only reduces noise effects but also decreases the overall computational complexity. On the other hand, the residual between the ground truth and the produced images replaces the produced images as input to the discriminator network, and a 2D zero mean Gaussian noise with controllable low variance replaces the real images as another input to the discriminator network. By leveraging the feature representation and properties of the 2D zero mean Gaussian noise, we restrict the optimization space to produce an SR image. Therefore, the residual of the generated SR images tend to approximates to a Gaussian noise, which introduces useless deviation information as little as possible. Experimental results show that RD-SRGAN can benefit from these strategies and achieve improved fidelity and naturalness comparison to existing methods. Switching the pretrained AutoEncoder allows the method to deal with images from diverse categories, e.g., remote sensing satellite imaging, medical imaging, and astronomy.

Person re-identification has been widely studied due to its importance in surveillance and forensics applications. In practice, gallery images are high-resolution (HR) while probe images are usually low-resolution (LR) in the identification scenarios with large variation of illumination, weather or quality of cameras. Person re-identification in this kind of scenarios, which we call super-resolution (SR) person re-identification, has not been well studied. In this paper, we propose a semi-coupled low-rank discriminant dictionary learning (SLD2L) approach for SR person re-identification. For the given training image set which consists of HR gallery and LR probe images, we aim to convert the features of LR images into discriminating HR features. Specifically, our approach learns a pair of HR and LR dictionaries and a mapping from the features of HR gallery images and LR probe images. To ensure that the converted features using the learned dictionaries and mapping have favorable discriminative capability, we design a discriminant term which requires the converted HR features of LR probe images should be close to the features of HR gallery images from the same person, but far away from the features of HR gallery images from different persons. In addition, we apply low-rank regularization in dictionary learning procedure such that the learned dictionaries can well characterize intrinsic feature space of HR and LR images. Experimental results on public datasets demonstrate the effectiveness of SLD2L.

Person re-identification (REID) is an important task in video surveillance and forensics applications. Many previous works often build models on the assumption that they have same resolution cross different camera views, while it is divorced from reality. To increase the adaptability of person REID models, this paper focuses on the low-resolution person REID task to relax the impractical assumption when traditional low-resolution person REID models are under pixel-to-pixel supervision in low and high resolution pedestrian image pairs. In addition, they are easily influenced by the global background, illumination or pose variations across camera views. Therefore, we propose a Part-based Enhanced Super Resolution (PESR) network by employing a part division strategy and an enhanced generative adversarial network to boost the unpaired pedestrian image super resolution process. Specifically, the part-based super resolution network transforms low resolution image in probe into high resolution without any pixel-to-pixel supervision and the part-based synthetic feature extractor module can learn discriminative pedestrian feature representation for the generated high resolution images, which employ a part feature connection loss as constraint to conduct matching for person re-identification. Furthermore, evaluations on four public person REID datasets demonstrate the advantages of our method over the state-of-the-art ones.

Person re-identification has been widely studied due to its importance in surveillance and forensics applications. In practice, gallery images are high resolution (HR), while probe images are usually low resolution (LR) in the identification scenarios with large variation of illumination, weather, or quality of cameras. Person re-identification in this kind of scenarios, which we call super-resolution (SR) person re-identification, has not been well studied. In this paper, we propose a semi-coupled low-rank discriminant dictionary learning (SLD2L) approach for SR person re-identification task. With the HR and LR dictionary pair and mapping matrices learned from the features of HR and LR training images, SLD2L can convert the features of the LR probe images into HR features. To ensure that the converted features have favorable discriminative capability and the learned dictionaries can well characterize intrinsic feature spaces of the HR and LR images, we design a discriminant term and a low-rank regularization term for SLD2L. Moreover, considering that low resolution results in different degrees of loss for different types of visual appearance features, we propose a multi-view SLD2L (MVSLD2L) approach, which can learn the type-specific dictionary pair and mappings for each type of feature. Experimental results on multiple publicly available data sets demonstrate the effectiveness of our proposed approaches for the SR person re-identification task.

行人再识别通过大时空范围内跨摄像机目标行人图像的检索与匹配，可实现人脸等生物特征失效情况下的行人关联，已成为智能视频监控系统的关键环节和支撑技术，并在智慧公安、智慧城市等国民经济建设中发挥了重要作用。近年行人再识别技术吸引了越来越多的关注，并取得了快速发展与进步。本文在对行人再识别技术进行简介的基础上，面向行人再识别的技术发展和落地应用需求与挑战，总结分析遮挡行人再识别、无监督行人再识别、虚拟数据生成、域泛化行人再识别、换装行人再识别、跨模态行人再识别和行人搜索等热点方向的前沿进展，归纳其发展现状和存在问题，最后对行人再识别技术的发展趋势进行展望。希望通过总结和分析，能够为研究人员开展行人再识别相关研究、推动行人再识别技术进步提供参考。;Person re-Identification(person re-ID) technique aims to solve the problem of association and matching of target person images across multiple cameras within a camera network of a surveillance system, especially in the case of face, iris and other biometrics recognition failure under non-cooperative application scenarios, and has become one of the key component and supporting technique for intelligent video surveillance systems and applications in intelligent public security and smart cities. Recently, person re-ID has attracted more and more attention from both academia and industry, and has made rapid development and progress. To meet the requirement of its technical challenges and application needs of person re-ID in practical scenarios, this paper will first give a brief introduction to the development history, commonly used datasets and evaluation metrics. Then, the recent progress in hot research topics of person re-ID is extensively reviewed and analyzed, which includes:occluded person re-ID, unsupervised person re-ID, virtual data generation, domain generalization, clothchanging person re-ID, cross-modal person re-ID and person search. First, to address the problem of impact of possible occlusions on the performance of person re-ID, recent progress in occluded person re-ID is first reviewed, in which the popular datasets for occluded person re-ID are briefly introduced, and the two major categories of occluded person re-ID models are then further reviewed. Second, facing the challenges of low-efficiency and high-cost data annotation and great impact of training data on the performance of person re-ID, unsupervised person re-ID and virtual data generation for person re-ID emerges as two hot topics in person re-ID. The paper elaborates the recent advances of unsupervised person reID, which can be classified into three major categories:pseudo label generation-based models, domain transfer-based models, and other related models, which take into consideration of the extra information like time-stamps, camera labels besides person image. Third, the-state-of-the-art works on virtual data generation for person re-ID are reviewed, with detailed introduction and performance comparisons of major virtual datasets. Fourth, recent researches on domain generalization person re-ID will be reviewed, which are classified into five categories:batch/instance normalization models, domain invariant feature learning models, deep-learning-based explicit image matching models, models based on mixture of experts and meta-learning-based models. Fifth, since most current person re-ID models largely depend on the color appearance of persons' clothes, clothes-changing person re-ID becomes a challenging setting, in which person images can exhibit large intra-class variation and small inter-class variation. Typical cloth-changing person re-ID datasets are introduced and the recent researches will then be reviewed, in which models in the first category explicitly introduces extra cloth-appearance-independent features like contour and face while the second try to decouple the cloth features and person ID features. Sixth, to compensate the drawbacks of conventional person re-ID of visible light/RGB images in natural complex scenes like poor lighting conditions in the night, the-state-of-the-art of cross-modal person re-ID, which aims to resolve the problem through other visible RGB images-excluded heterogeneous data, are reviewed, with a brief introduction of commonly used cross-modal person re-ID datasets first and then four sub-categories models according to the different modalities employed, including:RGB-infrared image person re-ID, RGB image-text person re-ID, RGB image-sketch person re-ID, and RGB-depth image person re-ID, respectively. Seventh, since existing person re-ID benchmarks and methods mainly focus on matching cropped person images between queries and candidates and is different from practical scenarios where the bounding box annotations of persons are often unavailable, person search, which jointly considers person detection and person re-ID in a single framework, becomes a new hot research topic. The typical datasets and recent progress on person search are reviewed. Finally, the existing challenges and development trend of person re-ID techniques are discussed. It is hoped that the summary and analysis can provide reference for relevant researchers to carry out research on person re-ID and promote the progress of person re-ID techniques and applications.

The number of existing person re-identification datasets is limited, and there are a series of changes such as illumination, background occlusion and pose among each dataset, which makes it difficult for the existing methods to learn robust feature representation, leading to a decline in recognition performance. To solve these problems, a person re-identification method combining style and pose generation is proposed in this paper. First with the impact of camera style differences in collecting images from different cameras, a style transformation method based on generative adversarial network is introduced into a person re-identification model, and cyclic generative adversarial networks (CycleGAN) is used to realize style transfer and reduce the influence of camera differences. Second, in view of the problem that when pedestrian pose changes greatly, easy to ignore identity-sensitive related information, AlphaPose is introduced to implement pose estimation. Combining style and posture for the first time and using improved deep convolution generative adversarial network (DCGAN) structure enrich the input sample information and generate unified style pose image; while using new synthetic data to train person re-identification network model improves the recognition performance of the model. Finally, further introducing random erasure method during data enhancement, in order to reduce the overfitting phenomena, improves the generalization ability of the network simultaneously and solves partial occlusion. The experimental results show that the proposed method outperforms typical style-based or pose-based methods. The accuracy of rank-1 and mAP on Market-1501 dataset is 90.4% and 74.5%, respectively, which are 2.28% and 5.78% higher, respectively. To a certain extent, the performance of person re-identification is improved.

In real-life scenarios, the accuracy of person re-identification (Re-ID) is subject to the limitation of camera hardware conditions and the change of image resolution caused by factors such as camera focusing errors. People call this problem cross-resolution person Re-ID. In this paper, we improve the recognition accuracy of cross-resolution person Re-ID by enhancing the image enhancement network and feature extraction network. Specifically, we treat cross-resolution person Re-ID as a two-stage task: the first stage is the image enhancement stage, and we propose a Super-Resolution Dual-Stream Feature Fusion sub-network, named SR-DSFF, which contains SR module and DSFF module. The SR-DSFF utilizes the SR module recovers the resolution of the low-resolution (LR) images and then obtains the feature maps of the LR images and super-resolution (SR) images, respectively, through the dual-stream feature fusion with learned weights extracts and fuses feature maps from LR and SR images in the DSFF module. At the end of SR-DSFF, we set a transposed convolution to visualize the feature maps into images. The second stage is the feature acquisition stage. We design a global-local feature extraction network guided by human pose estimation, named FENet-ReID. The FENet-ReID obtains the final features through multistage feature extraction and multiscale feature fusion for the Re-ID task. The two stages complement each other, making the final pedestrian feature representation has the advantage of accurate identification compared with other methods. Experimental results show that our method improves significantly compared with some state-of-the-art methods.

Person re-identification has recently attracted increasing interest in the computer vision and safety-critical applications. In practice, due to poor quality of cameras or long distance away from person, the captured pedestrian videos usually suffer from low resolution, which will result in the loss of useful information contained in videos and make person re-identification between low-resolution (LR) and high-resolution (HR) videos (PRLHV) be a challenging task. However, the problem of PRLHV has not been well studied. In this paper, we propose a semi-coupled mapping based set-to-set distance learning (SMDL) approach for PRLHV. Specifically, by regarding each video as a set of features extracted from several walking cycles, we learn a discriminative set-to-set distance metric to enhance the separability between videos from different persons. To decrease the influence of low resolution on the distance learning, we design a clustering-based semi-coupled mapping term for our approach, which can reduce the variation between features of low-resolution and high-resolution videos by a semi-coupled mapping matrix. Since there exists no low-resolution video pedestrian re-identification dataset under real-world scenario up to now, we contribute a benchmark dataset for PRLHV, named high-resolution and low-resolution video person re-identification dataset (HLVID). Although this dataset is challenging and difficult for person re-identification, it is a useful attempt for further studies on low-resolution video-based pedestrian re-identification under the real-world scene. The extensive experiments on the newly collected video dataset demonstrate that our approach performs better than the state-of-the-art person re-identification methods in the PRLHV task.

Person images captured by unconstrained surveillance cameras often have low resolutions (LR). This causes the resolution mismatch problem when matched against the high-resolution (HR) gallery images, negatively affecting the performance of person re-identification (re-id). An effective approach is to leverage image super-resolution (SR) along with person re-id in a joint learning manner. However, this scheme is limited due to dramatically more difficult gradients backpropagation during training. In this paper, we introduce a novel model training regularisation method, called Inter-Task Association Critic (INTACT), to address this fundamental problem. Specifically, INTACT discovers the underlying association knowledge between image SR and person re-id, and leverages it as an extra learning constraint for enhancing the compatibility of SR model with person re-id in HR image space. This is realised by parameterising the association constraint which enables it to be automatically learned from the training data. Extensive experiments validate the superiority of INTACT over the state-of-the-art approaches on the cross-resolution re-id task using five standard person re-id datasets.

Person re-identification that aims at matching individuals across multiple camera views has become indispensable in intelligent video surveillance systems. It remains challenging due to the large variations of pose, illumination, occlusion and camera viewpoint. Feature representation and metric learning are the two fundamental components in person reidentification. In this paper, we present a Special Dense Convolutional Neural Network (SD-CNN) to extract the feature and apply Joint Bayesian to measure the similarity of pedestrian image pairs. The SD-CNN can preserve more horizontal information to against viewpoint changes, maximize the feature reuse and ensure feature distributing discriminative. Joint Bayesian models the extracted feature representation as the sum of inter- and intra-personal variations, and the joint probability of two images being a same person can be obtained through log-likelihood ratio. Experiments show that our approach significantly outperforms state-of-the-art methods on several benchmarks of person re-identification.

In real-world surveillance systems, the person images captured by the camera network consists of various low-resolution (LR) images. It creates a resolution mismatching problem when compared against high-resolution images of a targeted person. It significantly affects the performance of person re-Identification. This problem is known as Low-Resolution Person re-identification (LR PREID). An efficient strategy would be to exploit image super-resolution (SR) with person re-identification as a mutual learning approach. In this paper, we propose a novel method MSA-SR-PREID to solve this problem. The model takes low-resolution images on different resolutions and resized them to pre-defined fixed resolution. The design of the super-resolution network consists of ESRGAN and the de-Noising module to generate super-resolution images. The SR images are later passed to the re-identification network to learn the unique descriptors to recognize a person identity. The performance of this model is evaluated on four competitive benchmarks, MLR-VIPeR, MLR-DukeMTMC-reID, VR-MSMT17, and VR-Market1501. The comparison with similar state-of-the-art demonstrates the superiority of our model.

The Low Resolution Face Recognition (LR FR) issue focuses on the resolution variation problem mainly displayed in surveillance systems where faces are captured in an unconstrained environment. To address this challenging problem, we introduced a new method to recognize faces under Low and Very Low Resolution conditions. The proposed method consists of two phases : an off-line phase and an inference phase. In the off-line phase, we generate a model to transform Low-Resolution (LR) face images into High-Resolution (HR) face images via a Generative Adversarial Network (GAN). As for the inference phase, we make use of the already generated model to improve the face resolution then we extract deep features to identify the face. An experimental study was carried out on the famous ORL and FERET databases and the obtained results proved the efficiency of the proposed method to deal with Low Resolution (LR) and Very Low Resolution (VLR) Face Recognition problem.

Car plate recognition is used in traffic monitoring and control systems such as intelligent parking lot management, finding stolen vehicles, and automated highway toll. In practice, Low-Resolution (LR) images or videos are widely used in surveillance systems. In low resolution surveillance systems, the car plate text is often illegible. Super-Resolution (SR) techniques can be used to improve the car plate quality by processing a series of LR images into a single High-Resolution (HR) image. Recovering the HR image from a single LR is still an ill-conditioned problem for SR. Previous methods always minimize the mean square loss in order to improve the peak signal to noise ratio (PSNR). However, minimizing the mean square loss leads to overly smoothed reconstructed image. In this paper, Generative Adversarial Networks (GANs) based SR is proposed to reconstruct the LR images into HR images. Besides that, perceptual loss is proposed to solve the smoothing issue. The quality of the GAN based SR generated images is compared to existing techniques such as bicubic, nearest and Super-Resolution Convolution Neural Network (SRCNN). The results show that the reconstructed images using GANs based SR achieve better results in term of perceptual quality compared to previous methods.

To solve the problem of super-resolution reconstruction in the two-dimensional barcode image, this paper applies the technique of super-resolution reconstruction based on sparse representation into this area. Given the characteristics of the two-dimensional barcode image, this paper presents a new approach which selects the orientation gradient and the gradient texture feature as reconstruction features for recovery. Through analyzing the edge characteristics of this kind of image, it is found that the directional derivatives of it are distinct. Thus the Krisch operator is adopted to get the edge gradient of the image as one feature for reconstruction. Besides, the edge direction texture is regarded as another feature for reconstruction because of the distinct texture directivity of the objective image. Therefore, the geometric information as well as the textural information of the image is taken into consideration for reconstruction. The experimental result shows that the proposed algorithm in this paper can effectively reconstruct the input low-resolution barcode image into the corresponding high-resolution one. What's more, compared with other similar super-resolution algorithms, this proposed algorithm improves the quality of the recovery image to a certain extent.