Partial Person Re-identification Research Articles

Attribute-Guided Collaborative Learning for Partial Person Re-Identification.

Partial person re-identification (ReID) aims to solve the problem of image spatial misalignment due to occlusions or out-of-views. Despite significant progress through the introduction of additional information, such as human pose landmarks, mask maps, and spatial information, partial person ReID remains challenging due to noisy keypoints and impressionable pedestrian representations. To address these issues, we propose a unified attribute-guided collaborative learning scheme for partial person ReID. Specifically, we introduce an adaptive threshold-guided masked graph convolutional network that can dynamically remove untrustworthy edges to suppress the diffusion of noisy keypoints. Furthermore, we incorporate human attributes and devise a cyclic heterogeneous graph convolutional network to effectively fuse cross-modal pedestrian information through intra- and inter-graph interaction, resulting in robust pedestrian representations. Finally, to enhance keypoint representation learning, we design a novel part-based similarity constraint based on the axisymmetric characteristic of the human body. Extensive experiments on multiple public datasets have shown that our model achieves superior performance compared to other state-of-the-art baselines.

Focusing on shared areas for partial person re-identification

ABSTRACT Person re-identification (Re-ID) can achieve ideal performance based on the prerequisite that the sampling image is complete. However, the whole body cannot be detected because pedestrians may be occluded or are at the edge of the surveillance range in real-world scenarios. Consequently, the image only contains part of the visible information of the pedestrian. When using the standard person re-identification to match the partial image with the complete one, we witness the problem of spatial misalignment and interference caused by missing areas. Hence, we propose a focused shared area model (FSA) for partial re-identification to solve such descriptive problems. We use self-supervised learning to locate the shared area and learn region-level features. In addition, we adopt self-attention mechanism to help the network visualize the important features of the image, thus reducing the influence of the background information. Finally, we verify the effectiveness of our method through experiments on two mainstream datasets: Market-1501, DukeMTMC-reID and two important partial datasets: Partial-REID and Partial-iLIDS.

Partial person re-identification using a pose-guided alignment network with mask learning

Partial person re-identification using a pose-guided alignment network with mask learning

Deep progressive attention for person re-identification

Person re-identification (Re-ID) aims to retrieve specific individuals across non-overlapping camera views. In recent years, attention-based models contribute to many computer vision tasks due to their great ability for learning discriminative features. We propose the deep progressive attention (DPA) in a more natural manner for person Re-ID. Similar to human visual attention mechanism, the proposed DPA progressively selects the most discriminative parts of a specific individual and formulates feature representation for comparison purpose. Concretely, on the one hand, the proposed DPA uses a long-term reward to optimize the discriminative feature selection. On the other hand, a deep convolutional architecture is integrated into a recurrent model for feature representation learning. Extensive experiments on three person Re-ID benchmarks Market-1501, DukeMTMC-reID, and CUHK03-NP demonstrate the proposed DPA is on par with the state-of-the-art. Moreover, the experiments on partial person Re-ID datasets indicate the proposed DPA is competitive with the specially designed partial person Re-ID methods.

DCR: A Unified Framework for Holistic/Partial Person ReID

According to the occlusions, Person reidentification (ReID) can be divided into holistic person ReID and partial person ReID tasks. Occlusions commonly exist in the partial person ReID task but pose or observation perspective changes often occur in the holistic person ReID task; thus, many different network architectures have been designed for each task. However, this approach increases the cost in practice and hinders the development of ReID techniques. To solve this problem, in this work, a unified framework is proposed for holistic/partial person ReID, which can effectively and efficiently address changes in pose or observation perspective and the occlusions in both tasks. In detail, we first employ a fully convolutional network to generate feature maps for an arbitrarily sized image and then use spatial pyramid pooling to obtain its spatial pyramid feature. Thereafter, to efficiently solve the matching problem between the query image and gallery images, we build a deep spatial pyramid feature collaborative reconstruction model (DCR). Experimental results on two partial person ReID datasets and three holistic person ReID datasets demonstrate that DCR outperforms state-of-the-art approaches on both tasks and all datasets. Specifically, it outperforms all competitors with a large margin and achieves an improvement of 9.07% and 5.95% over DSR on Partial ReID and Partial-iLIDS datasets with Rank-1, respectively. Similarly, it also achieves an improvement of 5.08% over VPM on the DukeMTMC-ReID dataset with Rank-1. Additionally, the running time of our method for each query is more than 7× faster than that of DSR or DuATM methods.

STNReID: Deep Convolutional Networks With Pairwise Spatial Transformer Networks for Partial Person Re-Identification

Partial person re-identification (ReID) is a challenging task because only partial information of person images is available for matching target persons. Few studies, especially on deep learning, have focused on matching partial person images with holistic person images. This study presents a novel deep partial ReID framework based on pairwise spatial transformer networks (STNReID), which can be trained on existing holistic person datasets. STNReID includes a spatial transformer network (STN) module and a ReID module. The STN module samples an affined image (a semantically corresponding patch) from the holistic image to match the partial image. The ReID module extracts the features of the holistic, partial, and affined images. Competition (or confrontation) is observed between the STN module and the ReID module, and two-stage training is applied to acquire a strong STNReID for partial ReID. Experimental results show that our STNReID obtains 66.7% and 54.6% rank-1 accuracies on partial ReID and partial iLIDS datasets, respectively. These values are at par with those obtained with state-of-the-art methods.

Semantic-Aware Occlusion-Robust Network for Occluded Person Re-Identification

In recent years, deep learning-based person re-identification (Re-ID) methods have made significant progress. However, the performance of these methods substantially decreases when dealing with occlusion, which is ubiquitous in realistic scenarios. In this article, we propose a novel semantic-aware occlusion-robust network (SORN) that effectively exploits the intrinsic relationship between the tasks of person Re-ID and semantic segmentation for occluded person Re-ID. Specifically, the SORN is composed of three branches, including a local branch, a global branch, and a semantic branch. In particular, the local branch extracts part-based local features, and the global branch leverages a novel spatial-patch contrastive loss (SPC) to extract occlusion-robust global features. Meanwhile, the semantic branch generates a foreground-background mask for a pedestrian image, which indicates the non-occluded areas of the human body. The three branches are jointly trained in a unified multi-task learning network. Finally, pedestrian matching is performed based on the local features extracted from the non-occluded areas and the global features extracted from the whole pedestrian image. Extensive experimental results on a large-scale occluded person Re-ID dataset (i.e., Occluded-DukeMTMC) and two partial person Re-ID datasets (i.e., Partial-REID and Partial-iLIDS) show the superiority of the proposed method compared with several state-of-the-art methods for occluded and partial person Re-ID. We also demonstrate the effectiveness of the proposed method on two general person Re-ID datasets (i.e., Market-1501 and DukeMTMC-reID).

Partial person re-identification with two-stream network and reconstruction

Partial person re-identification is a challenging issue at present. However, affected by occlusions, features in person re-identification cannot be detected and the traditional person re-identification methods can not accurately deal with it. In order to solve this problem, we propose to match query and gallery by combining different modes from two-stream network with sparse reconstruction to realize partial person re-identification. For acquiring features, bilinear pooling is applied to fuse the two different modes from the appearance network and pose network aiming at better performance. For matching query and galley, the robust sparse representation reconstructs the features extracted by the network for flexible solution, using the parameters learned from galley. The reconstruction process achieves arbitrary size images in partial person re-identification. In addition, we extract mid-level feature and fuse it with the high-level feature for more accuracy. Experiments demonstrate the performance of the proposed method better compared with the methods of state-of-the-art person re-identification methods on dataset Market1501, CUHK03, DukeMTMC-reID and partial person dataset Partial-REID, Partial-iLIDS.

Dynamic Feature Matching for Partial Face Recognition.

Partial face recognition (PFR) in an unconstrained environment is a very important task, especially in situations where partial face images are likely to be captured due to occlusions, out-of-view, and large viewing angle, e.g., video surveillance and mobile devices. However, little attention has been paid to PFR so far and thus, the problem of recognizing an arbitrary patch of a face image remains largely unsolved. This study proposes a novel partial face recognition approach, called Dynamic Feature Matching (DFM), which combines Fully Convolutional Networks (FCNs) and Sparse Representation Classification (SRC) to address partial face recognition problem regardless of various face sizes. DFM does not require prior position information of partial faces against a holistic face. By sharing computation, the feature maps are calculated from the entire input image once, which yields a significant speedup. Experimental results demonstrate the effectiveness and advantages of DFM in comparison with state-of-the-art PFR methods on several partial face databases, including CAISA-NIR-Distance, CASIA-NIR-Mobile, and LFW databases. The performance of DFM is also impressive in partial person re-identification on Partial RE-ID and iLIDS databases. The source code of DFM can be found at https://github.com/lingxiao-he/dfm new.