Person Re-identification In Camera Networks Research Articles

A hybrid multilinear-linear subspace learning approach for enhanced person re-identification in camera networks

Several tasks in surveillance systems depend on camera networks, one of them is Person re-identification (PRe-ID) which has wide interest as a research topic in the computer vision field. The current techniques in this issue encounter many obstacles in handling the variability of appearance, extracting effective features, and capturing intricate data relationships, leading to suboptimal performance causing systems to have low performance. This paper presents a novel hybrid subspace learning approach for Person re-identification (PRe-ID), that leverages the strengths of both multilinear and linear techniques to significantly enhance the performance of PRe-ID. The proposed process begins by extracting distinctive effective features from pedestrian images using LOMO and GOG as shallow feature descriptors with CNN for deep features, followed by the application of two stages of subspace learning. In the first stage, we employ a multilinear subspace learning algorithm known as Tensor-based Quadratic Discriminant Analysis (TXQDA). TXQDA captures complex relationships and interactions among different features, resulting in a more discriminative feature representation. Subsequently, we employ a linear subspace learning technique called Side Information Linear Discriminant Analysis (SILD) to refine the subspace learned from TXQDA. SILD utilizes linear projections to enhance discriminability and preserve crucial inter-class variations, thereby improving the overall performance of PRe-ID. Ultimately, we implement logistic regression (LR) fusion at the score level to enhance the performance of our hybrid subspace learning approach, particularly within various tensor feature representation scenarios. Extensive experiments are conducted on three challenging datasets to evaluate the effectiveness of our hybrid subspace learning approach. The experimental results demonstrate that our method achieves superior performance compared to state-of-the-art techniques in the field of PRe-ID.

Person Re-Identification Based on Heterogeneous Part-Based Deep Network in Camera Networks

In this paper, we propose a new deep learning model named heterogeneous part-based deep network for person re-identification in camera networks, which simultaneously learns the alignment and discrimination for parts of pedestrian images. Concretely, several parts are obtained through the uniform partition on the convolutional layer for each pedestrian image. Then, we present part-aligned distances to perform alignment by searching the shortest local distances between image parts in a certain range. Meanwhile, we utilize the batch hard triplet loss and cross-entropy loss to learn more discriminative part-based features in different aspects. Experiments are conducted on three challenging datasets, Market-1501, CUHK03, and DukeMTMC-reID, and we achieve 94.0%, 64.3%, and 83.6% rank-1 accuracy and 81.2%, 58.2%, and 68.0% mAP, outperforming the state-of-the-art methods by a large margin.

Adversarial Erasing Attention for Person Re-Identification in Camera Networks Under Complex Environments

Person re-identification (Re-ID) in camera networks under complex environments has achieved promising performance using deep feature representations. However, most approaches usually ignore to learn features from non-salient parts of pedestrian, which results in an incomplete pedestrian representation. In this paper, we propose a novel person Re-ID method named Adversarial Erasing Attention (AEA) to mine discriminative completed features using an adversarial way. Specifically, the proposed AEA consists of the basic network and the complementary network. On the one hand, original pedestrian images are used to train the basic network in order to extract global and local deep features. On the other hand, to learn features complementary to the basic network, we propose the adversarial erasing operation, that locates non-salient areas with the help of attention map, to generate erased pedestrian images. Then, we utilize them to train the complementary network and adopt the dynamic strategy to match the dynamic status of AEA in the learning process. Hence, the diversity of training samples is enriched and the complementary network could discover new clues when learning deep features. Finally, we combine the features learned from the basic and complementary networks to represent the pedestrian image. Experiments on three databases (Market1501, CUHK03 and DukeMTMC-reID) demonstrate the proposed AEA achieves great performances.

Learning local embedding deep features for person re-identification in camera networks

In this paper, we propose a novel feature learning method named local embedding deep features (LEDF) for person re-identification in camera networks. In order to learn the structural information of pedestrian, we first utilize the verification network that does not require explicit identity labels to obtain the local summing maps. We then combine all local summing maps of a pedestrian image to form the holistic summing map which has the same identity label with the original pedestrian image. Finally, we take the holistic summing maps as the input to train the identification network, and then obtain the LEDF from the last fully connected layer. The proposed LEDF fully considers the structural information by learning the local features and meanwhile possesses strong discriminative ability by learning global features. The experimental results on two large-scale datasets (Market-1501 and CUHK03) demonstrate that the proposed LEDF achieves better results than the state-of-the-art methods.

Learning deep features from body and parts for person re-identification in camera networks

In this paper, we propose to learn deep features from body and parts (DFBP) in camera networks which combine the advantages of part-based and body-based features. Specifically, we utilize subregion pairs to train the part-based feature learning model and predict whether they belong to positive subregion pairs. Meanwhile, we utilize holistic pedestrian images to train body-based feature learning model and predict the identities of the input images. In order to further improve the discrimination of features, we concatenate the part-based and body-based features to form the final pedestrian representation. We evaluate the proposed DFBP on two large-scale databases, i.e., Market1501 database and CUHK03 database. The results demonstrate that the proposed DFBP outperforms the state-of-the-art methods.

Integration Convolutional Neural Network for Person Re-Identification in Camera Networks

In this paper, we propose a novel deep model named integration convolutional neural network (ICNN) for person re-identification in camera networks, which jointly learns global and local features in a unified framework. To this end, the proposed ICNN simultaneously applies two kinds of loss functions. Specifically, we propose the soft triplet loss to learn global features which automatically adjusts the margin threshold within one batch. The soft triplet loss could alleviate the difficult in tuning parameters and therefore learns discriminative global features. In order to avoid the part misalignment problem, we learn latent local features by conducting local horizontal average pooling on the convolutional maps. Afterward, we implement the identification task on each local feature. We concatenate global and local features using a weighted strategy to present the pedestrian images. We evaluate the proposed ICNN on three large-scale databases. Our method achieves rank-1 accuracy of 92.13% on Market 1501, 61.4% on CUHK03 and 85.3% on DukeMTMC-reID, and the results outperform the state-of-the-art methods.

Appearance-based person reidentification in camera networks: problem overview and current approaches

Recent advances in visual tracking methods allow following a given object or individual in presence of significant clutter or partial occlusions in a single or a set of overlapping camera views. The question of when person detections in different views or at different time instants can be linked to the same individual is of fundamental importance to the video analysis in large-scale network of cameras. This is the person reidentification problem. The paper focuses on algorithms that use the overall appearance of an individual as opposed to passive biometrics such as face and gait. Methods that effectively address the challenges associated with changes in illumination, pose, and clothing appearance variation are discussed. More specifically, the development of a set of models that capture the overall appearance of an individual and can effectively be used for information retrieval are reviewed. Some of them provide a holistic description of a person, and some others require an intermediate step where specific body parts need to be identified. Some are designed to extract appearance features over time, and some others can operate reliably also on single images. The paper discusses algorithms for speeding up the computation of signatures. In particular it describes very fast procedures for computing co-occurrence matrices by leveraging a generalization of the integral representation of images. The algorithms are deployed and tested in a camera network comprising of three cameras with non-overlapping field of views, where a multi-camera multi-target tracker links the tracks in different cameras by reidentifying the same people appearing in different views.