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

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.

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.

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.

Many features have been proposed to improve the accuracy of person re-identification. Due to the illumination and viewpoint changes between different cameras, individual feature is less discriminative to separate different persons. In this paper, we propose a saliency-weighted feature descriptor and global-local fusion optimization for person re-identification. Firstly, the weights on pixels are calculated via saliency detection method, then the computed weights are integrated into local maximal occurrence (LOMO) feature descriptor. Secondly, the saliency weights are used to update the metric learning distance in training so that we can learn a new metric matrix for testing. And then, the whole person image is divided into upper and lower halves. A novel global-local fusion method is proposed to combine local and global regions together in the most appropriate way. After that an optimization algorithm is proposed to learn the weights among upper half, lower half and the whole image. According to those weights, a final fused distance is obtained. Experimental results show that the proposed method outperforms many state-of-the-art person re-identification methods.

Person re-identification aims to match the same pedestrians across different camera views and has been applied to many important applications such as intelligent video surveillance. Due to the spatiotemporal uncertainty and visual ambiguity of pedestrian image pairs, person re-identification remains a difficult and challenging problem. The huge success of deep learning has focused attention on the use of deep features for person re-identification. However, for person re-identification, most deep learning methods minimize cross-entropy or triplet-based losses, thereby neglecting the fact that the similarities and differences between image pairs can be considered simultaneously to increase discrimination. In this paper, we propose a novel deep learning method called deep similarity feature learning DSFL to extract more effective deep features for image pairs. Extensive experiments on two representative person re-identification datasets CUHK-03 and GRID demonstrate the effectiveness and robustness of DSFL.

Person re-identification is concerned with matching people across disjointed camera views at different places and different time instants. This task results of great interest in computer vision, especially in video surveillance applications where the re-identification and tracking of persons are required on uncontrolled crowded spaces and after long time periods. The latter aspects are responsible for most of the current unsolved problems of person re-identification, in fact, the presence of many people in a location as well as the passing of hours or days give arise to important visual appearance changes of people, for example, clothes, lighting, and occlusions; thus making person re-identification a very hard task. In this paper, for the first time in the state-of-the-art, a meta-feature based Long Short-Term Memory (LSTM) hashing model for person re-identification is presented. Starting from 2D skeletons extracted from RGB video streams, the proposed method computes a set of novel meta-features based on movement, gait, and bone proportions. These features are analysed by a network composed of a single LSTM layer and two dense layers. The first layer is used to create a pattern of the person’s identity, then, the seconds are used to generate a bodyprint hash through binary coding. The effectiveness of the proposed method is tested on three challenging datasets, that is, iLIDS-VID, PRID 2011, and MARS. In particular, the reported results show that the proposed method, which is not based on visual appearance of people, is fully competitive with respect to other methods based on visual features. In addition, thanks to its skeleton model abstraction, the method results to be a concrete contribute to address open problems, such as long-term re-identification and severe illumination changes, which tend to heavily influence the visual appearance of persons.

Person re-identification has important applications in video surveillance. It is particularly challenging because observed pedestrians undergo significant variations across camera views, and there are a large number of pedestrians to be distinguished given small pedestrian images from surveillance videos. This chapter discusses different approaches of improving the key components of a person re-identification system, including feature design, feature learning, and metric learning, as well as their strength and weakness. It provides an overview of various person re-identification systems and their evaluation on benchmark datasets. Multiple benchmark datasets for person re-identification are summarized and discussed. The performance of some state-of-the-art person identification approaches on benchmark datasets is compared and analyzed. It also discusses a few future research directions on improving benchmark datasets, evaluation methodology, and system design.

This paper introduces a groundbreaking approach to Person Re-identification (Re-ID), significantly enhancing accuracy and robustness through contrastive learning, especially under challenging real-world conditions like occlusions. The proposed method addresses the limitations of traditional supervised learning, which often demands extensive labeled data, and unsupervised learning, which can be less accurate due to issues such as occlusion. Our solution features a neural network designed to extract both global and local features essential for identifying individuals across different camera views. The network employs global pooling to generate overall identity representation and horizontal pooling to capture detailed local features crucial for distinguishing person identities. Additionally, we present a novel module that integrates a feature extraction encoder with an MLP to refine the comparison of features. To further enhance the model's performance, we utilize a contrastive loss function, an advancement over the standard InfoNCE Loss, which effectively differentiates between positive and negative samples. Experimental results demonstrate a significant improvement in Person Re-ID accuracy and reliability across various environments. By integrating global and local feature representation and leveraging contrastive learning, our model advances the state-of-the-art in Person Re-ID technology. This innovation has broad applications in video surveillance and automated monitoring systems, setting a new standard for accuracy and reliability in the field.

Person re-identification has become a hot research topic due to its importance in surveillance and forensics applications. The purpose of person re-identification is to find the same person from disjoint camera views at different time. Most of the existing methods try to identify the person by measuring the similarity of two still images from different camera views, which only uses intraimage features such as color, shape and texture. In this paper, we propose a person re-identification architecture which analyzes a sequence pair while not an image pair, so not only intra-image features but also the gait feature is also considered. In contrast to existing works that use handcrafted features, our method automatically learns spatio-temporal features optimal for the person re-identification task with a deep convolutional network. To learn a discriminant metric, we use a subspace and metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA).The process of XQDA is also called transfer learning. Experiments show that our method significantly outperforms the state of the art on both a large dataset (CUHK03) and a medium-sized dataset (CUHK01). We also get a better performance on a small dataset (VIPeR) with a pre-trained network without fine-tuning.

In video surveillance, person re-identification consists in recognizing a person or determine whether a given person has already appeared over a large-scale network of cameras. In general, the human appearance obtained in one camera is usually different from the others obtained in other cameras, because of variations in view angle, illumination, body pose, background clutter and occlusion. To address this problem, we propose a person re-identification approach by using fusing multiple human body parts signature method. Firstly we detect human and human body parts. secondly we extract discriminatingly features on each of the parts. Finally, we map the person re-identification issue to a distance learning problem, which aims to find out the similarity between corresponding body parts. The experimental result on several public benchmark datasets (Prid2011,ViPER,ETHZ) shows that the proposed approach works well and leads to a high accuracy of person re-identification.

Video-based person re-identification is one of the significant areas of computer vision, and it has huge application potential in video surveillance. Many existing methods address this difficulty by extracting only the characteristics of one person in the network. This paper proposes a Pose-Attribute network (MBNet) through multi-task learning of embedded pose representation, which combines pedestrian attribute recognition with ReID. MBNet extracts 18 human body keypoints and 12 pedestrian attributes that contain gender, shoes, etc. from the image, and simultaneously learns the local and global features. Conclusion of experiments is on two datasets, including the DukeMTMC-VID and MARS. The results show that the mAP of MBNet is higher than the mAP of pose-based or attribute aware for Person re-identification. The experimental results show that the proposed network is better than other video-based people re-identification approaches.

Automatic Person Re-identification by video surveillance is commonly used in different applications. Perhaps the human uniqueness criteria for tracking the presence of the same person across multiple camera views and a person’s growth identification is extremely challenging. To solve the above problem, we propose an efficient Auto Track Regression System (ATRF) based on a deep learning technique that uses an eminent representation strategy along with recognition. In this work, the Auto Wiley Detective (AWD) approach is proposed for the representation of features that can collect valuable information by monitoring individuals. After obtaining important information on the characteristics, it is possible to define the personal growth identity of the generation. The OPVC (Original Pick Virtual Classifier) is used for accurate classification of the queried person from a dense area by utilizing features of a person’s growth identity extracted from feature extraction by the Auto Wiley Detection Method. The proposed Originated Pick Virtual Classifier (OPVC) uses Platt scaling (originated pick) on probit regression (virtual) to train the featured data set for accurate person re-identification, which is boosted by the Karush–Kuhn–Tucker (KKT) conditions to reduce false re-identification. Since the gallery information is trained using the Backpropagation method and smoothened analysis through approximated output, the Auto Wiley Detection Method proficiently detects the required information automatically. This also helps to detect the person query image from the database, which contains a vast collection of video images based on the similarity features identified in the query image and the detailed features extracted from the query image. The classification is completed automatically, and then the Person Re-Identification from the databases is performed accurately and efficiently. Henceforth, the proposed work effectively extracts reliable height and age estimates with improved flexibility and individual re-identifying capabilities.

In this paper, we present a system for person re-identification in TV series. In the context of video retrieval, person re-identification refers to the task where a user clicks on a person in a video frame and the system then finds other occurrences of the same person in the same or different videos. The main characteristic of this scenario is that no previously collected training data is available, so no person-specific models can be trained in advance. Additionally, the query data is limited to the image that the user clicks on. These conditions pose a great challenge to the re-identification system, which has to find the same person in other shots despite large variations in the person's appearance. In the study, facial appearance is used as the re-identification cue, since, in contrast to surveillance-oriented re-identification studies, the person can have different clothing in different shots. In order to increase the amount of available face data, the proposed system employs a face tracker that can track faces up to full profile views. This makes it possible to use a profile face image as query image and also to retrieve images with non-frontal poses. It also provides temporal association of the face images in the video, so that instead of using single images for query or target, whole tracks can be used. A fast and robust face recognition algorithm is used to find matching faces. If the match result is highly confident, our system adds the matching face track to the query set. Finally, if the user is not satisfied with the number of returned results, the system can present a small number of candidate face images and lets the user confirm the ones that belong to the queried person. These features help to increase the variation in the query set, making it possible to retrieve results with different poses, illumination conditions, etc. The system is extensively evaluated on two episodes of the TV series Coupling, showing very promising results.

Pedestrian attributes (such as gender, age, hairstyle, and clothing) can effectively represent the appearance of pedestrians. These are high-level semantic features that are robust to illumination, deformation, etc. Therefore, they can be widely used in person re-identification, video structuring analysis and other applications. In this paper, a pedestrian attributes recognition method for surveillance scenarios using a multi-task lightweight convolutional neural network is proposed. Firstly, the labels of the attributes for each pedestrian image are integrated into a label vector. Then, a multi-task lightweight Convolutional Neural Network (CNN) is designed, which consists of five convolutional layers, three pooling layers and two fully connected layers to extract the deep features of pedestrian images. Considering that the data distribution of the datasets is unbalanced, the loss function is improved based on the sigmoid cross-entropy, and the scale factor is added to balance the amount of various attributes data. Through training the network, the mapping relationship model between the deep features of pedestrian images and the integration label vector of their attributes is established, which can be used to predict each attribute of the pedestrian. The experiments were conducted on two public pedestrian attributes datasets in surveillance scenarios, namely PETA and RAP. The results show that, compared with the state-of-the-art pedestrian attributes recognition methods, the proposed method can achieve a superior accuracy by 91.88% on PETA and 87.44% on RAP respectively.

As a result of its important role in video surveillance, pedestrian attribute recognition has become an attractive facet of computer vision research. Because of the changes in viewpoints, illumination, resolution and occlusion, the task is very challenging. In order to resolve the issue of unsatisfactory performance of existing pedestrian attribute recognition methods resulting from ignoring the correlation between pedestrian attributes and spatial information, in this paper, the task is regarded as a spatiotemporal, sequential, multi-label image classification problem. An attention-based neural network consisting of convolutional neural networks (CNN), channel attention (CAtt) and convolutional long short-term memory (ConvLSTM) is proposed (CNN-CAtt-ConvLSTM). Firstly, the salient and correlated visual features of pedestrian attributes are extracted by pre-trained CNN and CAtt. Then, ConvLSTM is used to further extract spatial information and correlations from pedestrian attributes. Finally, pedestrian attributes are predicted with optimized sequences based on attribute image area size and importance. Extensive experiments are carried out on two common pedestrian attribute datasets, PEdesTrian Attribute (PETA) dataset and Richly Annotated Pedestrian (RAP) dataset, and higher performance than other state-of-the-art (SOTA) methods is achieved, which proves the superiority and validity of our method.