Group Re-identification Research Articles

Separable Spatial-Temporal Residual Graph for Cloth-Changing Group Re-Identification.

Group re-identification (GReID) aims to correctly associate group images belonging to the same group identity, which is a crucial task for video surveillance. Existing methods only model the member feature representations inside each image (regarded as spatial members), which leads to potential failures in long-term video surveillance due to cloth-changing behaviors. Therefore, we focus on a new task called cloth-changing group re-identification (CCGReID), which needs to consider group relationship modeling in GReID and robust group representation against cloth-changing members. In this paper, we propose the separable spatial-temporal residual graph (SSRG) for CCGReID. Unlike existing GReID methods, SSRG considers both spatial members inside each group image and temporal members among multiple group images with the same identity. Specifically, SSRG constructs full graphs for each group identity within the batched data, which will be completely and non-redundantly separated into the spatial member graph (SMG) and temporal member graph (TMG). SMG aims to extract group features from spatial members, and TMG improves the robustness of the cloth-changing members by feature propagation. The separability enables SSRG to be available in the inference rather than only assisting supervised training. The residual guarantees efficient SSRG learning for SMG and TMG. To expedite research in CCGReID, we develop two datasets, including GroupPRCC and GroupVC, based on the existing CCReID datasets. The experimental results show that SSRG achieves state-of-the-art performance, including the best accuracy and low degradation (only 2.15% on GroupVC). Moreover, SSRG can be well generalized to the GReID task. As a weakly supervised method, SSRG surpasses the performance of some supervised methods and even approaches the best performance on the CSG dataset.

Unsupervised Group Re-identification via Adaptive Clustering-Driven Progressive Learning

Group re-identification (G-ReID) aims to correctly associate groups with the same members captured by different cameras. However, supervised approaches for this task often suffer from the high cost of cross-camera sample labeling. Unsupervised methods based on clustering can avoid sample labeling, but the problem of member variations often makes clustering unstable, leading to incorrect pseudo-labels. To address these challenges, we propose an adaptive clustering-driven progressive learning approach (ACPL), which consists of a group adaptive clustering (GAC) module and a global dynamic prototype update (GDPU) module. Specifically, GAC designs the quasi-distance between groups, thus fully capitalizing on both individual-level and holistic information within groups. In the case of great uncertainty in intra-group members, GAC effectively minimizes the impact of non-discriminative features and reduces the noise in the model's pseudo-labels. Additionally, our GDPU devises a dynamic weight to update the prototypes and effectively mine the hard samples with complex member variations, which improves the model's robustness. Extensive experiments conducted on four popular G-ReID datasets demonstrate that our method not only achieves state-of-the-art performance on unsupervised G-ReID but also performs comparably to several fully supervised approaches.

Uncertainty Modeling for Group Re-Identification

Uncertainty Modeling for Group Re-Identification

Dynamic Screening Strategy Based on Feature Graphs for UAV Object and Group Re-Identification

In contemporary times, owing to the swift advancement of Unmanned Aerial Vehicles (UAVs), there is enormous potential for the use of UAVs to ensure public safety. Most research on capturing images by UAVs mainly focuses on object detection and tracking tasks, but few studies have focused on the UAV object re-identification task. In addition, in the real-world scenarios, objects frequently get together in groups. Therefore, re-identifying UAV objects and groups poses a significant challenge. In this paper, a novel dynamic screening strategy based on feature graphs framework is proposed for UAV object and group re-identification. Specifically, the graph-based feature matching module presented aims to enhance the transmission of group contextual information by using adjacent feature nodes. Additionally, a dynamic screening strategy designed attempts to prune the feature nodes that are not identified as the same group to reduce the impact of noise (other group members but not belonging to this group). Extensive experiments have been conducted on the Road Group, DukeMTMC Group and CUHK-SYSU-Group datasets to validate our framework, revealing superior performance compared to most methods. The Rank-1 on CUHK-SYSU-Group, Road Group and DukeMTMC Group datasets reaches 71.8%, 86.4% and 57.8%, respectively. Meanwhile, our method performance is explored on the UAV datasets of PRAI-1581 and Aerial Image, the infrared datasets of SYSU-MM01 and CM-Group and the NIR dataset of RBG-NIR Scene dataset; the unexpected findings demonstrate the robustness and wide applicability of our method.

Siamese Transformer for Group Re-Identification via Multiscale Feature Transform and Joint Learning

Siamese Transformer for Group Re-Identification via Multiscale Feature Transform and Joint Learning

Pedestrian Group Re-Identification and Trajectory Prediction through Zone-based Clustering

Pedestrian Group Re-Identification and Trajectory Prediction through Zone-based Clustering

Patch-based camera-aware person-to-group learning and group similarity strategy for unsupervised group re-identification

In real life, people often go together in groups, which extends to an important but less researched task of computer vision: group re-identification. Differing from the related task of person re-identification, group re-identification involves unique challenges: self-occlusion, layout changes, membership changes, and the lack of labeled data. To solve these problems, we propose a patch-based camera-aware person-to-group unsupervised learning network. For the problem of high labeling costs, we train an unsupervised feature extraction network to extract the features of group members. Moreover, the feature extraction network is based on image patches, which solves the problem of group member alignment caused by occlusion within the group. To handle the large intra-ID variance resulting from changes in camera views, we add camera information to the model and propose the multi-grained camera-aware loss. For the problems of layout changes and membership changes, we adopt a person-to-group strategy. Specifically, we propose a novel group distance metric, named Modified Hausdorff Distance, to exploit the information of all the group members. Experimental results on two public datasets demonstrate the superiority of our approach.

A summary on group re-identification

A summary on group re-identification

Uncertainty Modeling with Second-Order Transformer for Group Re-identification

Group re-identification (G-ReID) focuses on associating the group images containing the same persons under different cameras. The key challenge of G-ReID is that all the cases of the intra-group member and layout variations are hard to exhaust. To this end, we propose a novel uncertainty modeling, which treats each image as a distribution depending on the current member and layout, then digs out potential group features by random samplings. Based on potential and original group features, uncertainty modeling can learn better decision boundaries, which is implemented by two modules, member variation module (MVM) and layout variation module (LVM). Furthermore, we propose a novel second-order transformer framework (SOT), which is inspired by the fact that the position modeling in the transformer is coped with the G-ReID task. SOT is composed of the intra-member module and inter-member module. Specifically, the intra-member module extracts the first-order token for each member, and then the inter-member module learns a second-order token as a group feature by the above first-order tokens, which can be regarded as the token of tokens. A large number of experiments have been conducted on three available datasets, including CSG, DukeGroup and RoadGroup. The experimental results show that the proposed SOT outperforms all previous state-of-the-art methods.

DotSCN: Group Re-Identification via Domain-Transferred Single and Couple Representation Learning

Group re-identification (G-ReID) is an important yet less-studied task. Its challenges not only lie in appearance changes of individuals, but also involve group layout and membership changes. To address these issues, the key task of G-ReID is to learn group representations robust to such changes. Nevertheless, unlike ReID tasks, there still lacks comprehensive publicly available G-ReID datasets, making it difficult to learn effective representations using deep learning models. In this article, we propose a Domain-Transferred Single and Couple Representation Learning Network (DotSCN). Its merits are two aspects: 1) Owing to the lack of labelled training samples for G-ReID, existing G-ReID methods mainly rely on unsatisfactory hand-crafted features. To gain the power of deep learning models in representation learning, we first treat a group as a collection of multiple individuals and propose transferring the representation of individuals learned from an existing labeled ReID dataset to a target G-ReID domain without a suitable training dataset. 2) Taking into account the neighborhood relationship in a group, we further propose learning a novel couple representation between two group members, that achieves better discriminative power in G-ReID tasks. In addition, we propose a weight learning method to adaptively fuse the domain-transferred individual and couple representations based on an L-shape prior. Extensive experimental results demonstrate the effectiveness of our approach that significantly outperforms state-of-the-art methods by 11.7% CMC-1 on the Road Group dataset and by 39.0% CMC-1 on the DukeMCMT dataset.

Part Relational Mean Model for Group Re-Identification

Most current research on pedestrian re-identification (ReID) is focusing on single-person ReID. However, people are rarely alone and often walk together in groups. Therefore, there is an urgent need to study the problem of group ReID (G-ReID). G-ReID is challenging because of the difficulties related to the differences in group appearance caused by changes in the group layout and membership. In this paper, we have proposed a part-based minus-average relational and arithmetic mean descriptor (PRM) algorithm to obtain a robust representation of groups. Based on local features, we have designed the arithmetic mean descriptor and the minus-average relational descriptor to solve the G-ReID problem caused by changes in the number of group members and their relative positions within the group. Moreover, the minus-average relational descriptor can also be used to describe the differences in the appearance of group members. Considering the rarity of G-ReID datasets and the need to improve the applicability of the G-ReID algorithm in real scenarios, we have collected a new dataset called the Bus Rapid Transit (BRT) G-ReID dataset. Extensive experimental results demonstrate the effectiveness of the PRM algorithm and indicate that it outperforms state-of-the-art algorithms by 7.5% for the cumulative matching feature (CMC-1) on the i-LIDS MCTS group dataset and by 19.4% for the CMC-1 on the Road Group dataset and it outperforms the baseline by 2.4% for the CMC-1 on the BRT dataset.

Harmonious attention network for person re-identification via complementarity between groups and individuals

Person re-identification (Re-ID) is of important capability for artificial intelligence and human–computer interaction. The main challenge of person Re-ID lies in limited data to precisely capture a wide range of appearance variations over multiple viewpoints. Furthermore, compared to the Re-ID between the single person, the person groups contain information about the relationship between pedestrians that can potentially help identify certain identities. The Re-ID combining groups and individuals remain to be a promising task under rare study. In this paper, we propose a harmonious attention network for person re-identification, in which we jointly consider the complementarity between person groups and individuals. Concretely, first we propose a two-stream attentive network (TSAN) to respectively learn the information from the person groups and individuals. TSAN consists of a spatial–temporal fusion network for the group Re-ID, as well as a deep network for the traditionally individual person Re-ID. To jointly consider the contributions of the groups and individuals, then we propose a novel re-ranking algorithm (GIRK) based on the learned features to associate the group and individual information. We also propose a new group Re-ID dataset DukeGroupVid to evaluate the performance of our approach. Comprehensive experimental results on the proposed dataset and other Re-ID datasets demonstrate the effectiveness of our model.

Group Re-Identification With Group Context Graph Neural Networks

Group re-identification aims to match groups of people across disjoint cameras. In this task, the contextual information from neighbor individuals can be exploited for re-identifying each individual within the group as well as the entire group. However, compared with single person re-identification, it brings new challenges including group layout and group membership changes. Motivated by the observation that individuals who are close together are more likely to keep in the same group under different cameras than those who are far apart, we propose to model each group as a spatial K-nearest neighbor graph (SKNNG) and design a group context graph neural network (GCGNN) for graph representation learning. Specifically, for each node in the graph, the proposed GCGNN learns an embedding which aggregates the contextual information from neighbor nodes. We design multiple weighting kernels for neighborhood aggregation based on the graph properties including node in-degrees and spatial relationship attributes. We compute the similarity scores between node embeddings of two graphs for group member association and obtain the matching score between the two graphs by summing up the similarity scores of all linked node pairs. Experimental results on three public datasets show that our approach performs favorably against state-of-the-art methods and achieves high efficiency.

Group Reidentification with Multigrained Matching and Integration.

The task of reidentifying groups of people under different camera views is an important yet less-studied problem. Group reidentification (Re-ID) is a very challenging task since it is not only adversely affected by common issues in traditional single-object Re-ID problems, such as viewpoint and human pose variations, but also suffers from changes in group layout and group membership. In this paper, we propose a novel concept of group granularity by characterizing a group image by multigrained objects: individual people and subgroups of two and three people within a group. To achieve robust group Re-ID, we first introduce multigrained representations which can be extracted via the development of two separate schemes, that is, one with handcrafted descriptors and another with deep neural networks. The proposed representation seeks to characterize both appearance and spatial relations of multigrained objects, and is further equipped with importance weights which capture variations in intragroup dynamics. Optimal group-wise matching is facilitated by a multiorder matching process which, in turn, dynamically updates the importance weights in iterative fashion. We evaluated three multicamera group datasets containing complex scenarios and large dynamics, with experimental results demonstrating the effectiveness of our approach.

From person to group re-identification via unsupervised transfer of sparse features

The visual association of a person appearing in the field of view of different cameras is today well known as Person Re-Identification. Current approaches find a solution to such a problem by considering persons as individuals, hence avoiding the fact that frequently they form groups or move in crowds. In such cases, the information acquired by neighboring individuals can provide relevant visual context to boost the performance in re-identifying persons within the group. In light of enriched information, groups re-identification encompasses additional problems to the common person re-identification ones, such as severe occlusions and changes in the relative position of people within the group. In this paper, the single person re-identification knowledge is transferred by means of a sparse dictionary learning to group re-identification. First, patches extracted from single person images are used to learn a dictionary of sparse atoms. This is used to obtain a sparsity-driven residual group representation that is exploited to perform group re-identification. To evaluate the performance of the proposed approach, we considered the i-LIDS groups dataset that is the only group re-identification publicly available dataset. The benchmark datasets for single person re-identification evaluation do not include group information, hence we collected two additional datasets under challenging scenarios and used them to validate our solution.