Low illumination person re-identification

Multimedia Tools and Applications - Low illumination is a common problem for recognition and tracking. Low illumination video-based person re identification (re-id) is an important application in...     

Combined salience based person re-identification

Human eye perceives an object as the entity with global information and local information. Human salience is distinctive local information in matching pedestrians across disjoint camera views, and matching on overall foreground guarantees reliable and robust identification. In this paper, we propose a strategy for the matching of mean salience to identify pedestrians. Also, we consider that person re-identification based on the local single directional matching suffers from the variations of pose, illumination and overlapping, and propose a global bi-directional matching to solve the challenging problems of person re-identification. Furthermore, our matching of mean salience is tightly combined with the global bi-directional matching. Patch matching is utilized to handle the misalignment problem in pedestrian images. We test our feature and matching approaches in person re-identification scenario. Experimental results demonstrate that the mean salience and the global bi-directional matching have promising discriminative capability in comparison with other ones.     

Gait recognition for person re-identification

The Journal of Supercomputing - Person re-identification across multiple cameras is an essential task in computer vision applications, particularly tracking the same person in different scenes....     

Local region partition for person re-identification

Multimedia Tools and Applications - Due to the different posture and view angle, the image will appear some objects that do not exist in another image of the same person captured by another camera....     

Gait-based person re-identification under covariate factors

Pattern Analysis and Applications - Gait is recognized as an effective behavioral biometric trait. Gait pattern information can be captured and perceived from a distance thanks to its noninvasive...     

Learning discriminative features for semi-supervised person re-identification

Multimedia Tools and Applications - We focus on the one-example person re-identification (Re-ID) task, where each identity has only one labeled example along with many unlabeled examples. Since...     

Robust person re-identification via graph convolution networks

Person re-identification (re-id) aims to identity the same person over multiple cameras; it has been successfully applied to various computer vision applications as a fundamental method. Owing to the development of deep learning, person re-id methods, which typically use triplet networks based on triplet loss, have demonstrated great success. However, the appearances of people are similar and hence difficult to distinguish in many cases. Therefore, we present a novel graph convolution network and enhances traditional triplet loss functions. Our method defines reference, positive, and negative features for triplet loss as three vertices of a graph, respectively, and adjusts their mutual distance through learning. The method adopts graph convolutions efficiently, thereby affording low computational costs. Experimental results demonstrate that our method is superior to the baseline on the Market-1501 dataset. The proposed GCN-based triplet loss considerably contributes to improve re-identification methods quantitatively and qualitatively.

     

Deep multi-instance learning for end-to-end person re-identification

Multimedia Tools and Applications - In this paper, we introduce a deep multi-instance learning framework to boost the instance-level person re-identification performance. Motivated by the...     

A part-based attention network for person re-identification

Multimedia Tools and Applications - Person re-identification (re-id) is the task of recognizing images of the same pedestrian captured by different cameras with non-overlapping views. Person re-id...     

Cross-modality person re-identification via channel-based partition network

Applied Intelligence - Visible-infrared cross-modality person re-identification is an important task in the night video surveillance system, the huge difference between infrared and visible light...     

On-the-fly feature importance mining for person re-identification

State-of-the-art person re-identification methods seek robust person matching through combining various feature types. Often, these features are implicitly assigned with generic weights, which are assumed to be universally and equally good for all individuals, independent of people's different appearances. In this study, we show that certain features play more important role than others under different viewing conditions. To explore this characteristic, we propose a novel unsupervised approach to bottom-up feature importance mining on-the-fly specific to each re-identification probe target image, so features extracted from different individuals are weighted adaptively driven by their salient and inherent appearance attributes. Extensive experiments on three public datasets give insights on how feature importance can vary depending on both the viewing condition and specific person's appearance, and demonstrate that unsupervised bottom-up feature importance mining specific to each probe image can facilitate more accurate re-identification especially when it is combined with generic universal weights obtained using existing distance metric learning methods.     

Mutual purification for unsupervised domain adaptation in person re-identification

Neural Computing and Applications - Unsupervised domain adaptation person re-identification aims to adapt the model learned on a labeled source domain to an unlabeled target domain. It has...     

Two-stage person re-identification scheme using cross-input neighborhood differences

The Journal of Supercomputing - Person re-identification aims to identify images of a particular person captured from different cameras or the same camera under different conditions. Person...     

Video-based person re-identification by semi-supervised adaptive stepwise learning

Pattern Analysis and Applications - Person re-identification (ReID) is mainly aimed at establishing correct identity correspondence among moving person collected by multiple cameras. Extending...     

Two-stream person re-identification with multi-task deep neural networks

Person re-identification (re-id) with images is very useful in video surveillance to find specific targets. However, it is challenging due to the complex variations of human poses, camera viewpoints, lighting, occlusion, resolution, background clutter and so on. The key to tackle this problem is how to represent the body and match these representations among frames. Current methods usually use the features of the whole bodies, and the performance may be reduced because of part invisibility. To solve this problem, we propose a two-stream strategy to use parts and bodies simultaneously. It utilizes a multi-task learning framework with deep neural networks (DNNs). Part detection and body recognition are performed as two tasks, and the features are extracted by two DNNs. The features are connected to multi-task learning to compute the mapping model from features to identifications. With this model, re-id can be achieved. Experimental results on a challenging task show the effectiveness of the proposed method.     

A Siamese inception architecture network for person re-identification

Person re-identification is an extremely challenging problem as person’s appearance often undergoes dramatic changes due to the large variations of viewpoints, illuminations, poses, image resolutions, and cluttered backgrounds. How to extract discriminative features is one of the most critical ways to address these challenges. In this paper, we mainly focus on learning high-level features and combine the low-level, mid-level, and high-level features together to re-identify a person across different cameras. Firstly, we design a Siamese inception architecture network to automatically learn effective semantic features for person re-identification in different camera views. Furthermore, we combine multi-level features in null space with the null Foley–Sammon transform metric learning approach. In this null space, images of the same person are projected to a single point, which minimizes the intra-class scatter to the extreme and maximizes the relative inter-class separation simultaneously. Finally, comprehensive evaluations demonstrate that our approach achieves better performance on four person re-identification benchmark datasets, including Market-1501, CUHK03, PRID2011, and VIPeR.     

Random linear interpolation data augmentation for person re-identification

Person Re-Identification (person re-ID) is an image retrieval task which identifies the same person in different camera views. Generally, a good person re-ID model requires a large dataset containing over 100000 images to reduce the risk of over-fitting. Most current handcrafted person re-ID datasets, however, are insufficient for training a learning model with high generalization ability. In addition, the lacking of images with various levels of occlusion is still remaining in most existing datasets. Motivated by these two problems, this paper proposes a new data augmentation method called Random Linear Interpolation that can enlarge the sizes of person re-ID datasets and improve the generalization ability of the learning model. The key enabler of our approach is generating fused images by interpolating pairs of original images. In other words, the innovation of the proposed approach is considering data augmentation between two random samples. Plenty of experimental results demonstrates that the proposed method is effective to improve baseline models. On Market1501 and DukeMTMC-reID datasets, our approach can achieve 92.71% and 82.19% rank-1 accuracy, respectively.

     

Multi-level feature learning with attention for person re-identification

Person re-identification (re-ID) aims to match a specific person in a large gallery with different cameras and locations. Previous part-based methods mainly focus on part-level features with uniform partition, which increases learning ability for discriminative feature but not efficient or robust to scenarios with large variances. To address this problem, in this paper, we propose a novel feature fusion strategy based on traditional convolutional neural network. Then, a multi-branch deeper feature fusion network architecture is designed to perform discriminative learning for three semantically aligned region. Based on it, a novel self-attention mechanism is employed to softly assign corresponding weights to the semantic aligned feature during back-propagation. Comprehensive experiments have been conducted on several large-scale benchmark datasets, which demonstrates that proposed approach yields consistent and competitive re-ID accuracy compared with current single-domain re-ID methods.