A Novel Framework for Data Augmentation on Appearance Changes for Long-Term Person Re-Identification

To address challenges related to the inadequate representation and inaccurate discrimination of pedestrian attributes, we propose a novel method for person re-identification, which leverages global feature learning and classification optimization. Specifically, this approach integrates a Normalization-based Channel Attention Module into the fundamental ResNet50 backbone, utilizing a scaling factor to prioritize and enhance key pedestrian feature information. Furthermore, dynamic activation functions are employed to adaptively modulate the parameters of ReLU based on the input convolutional feature maps, thereby bolstering the nonlinear expression capabilities of the network model. By incorporating Arcface loss into the cross-entropy loss, the supervised model is trained to learn pedestrian features that exhibit significant inter-class variance while maintaining tight intra-class coherence. The evaluation of the enhanced model on two popular datasets, Market1501 and DukeMTMC-ReID, reveals improvements in Rank-1 accuracy by 1.28% and 1.4%, respectively, along with corresponding gains in the mean average precision (mAP) of 1.93% and 1.84%. These findings indicate that the proposed model is capable of extracting more robust pedestrian features, enhancing feature discriminability, and ultimately achieving superior recognition accuracy.

Person re-identification (Re-ID) aims to retrieve a person of interest across multiple nonoverlapping cameras. In the last few years, the construction of aerial person Re-ID datasets has been appealing because visual surveillance through unmanned aerial vehicle (UAV) platforms has become very valuable in real-world scenarios. However, great differences are exhibited between the pedestrian images captured by ground cameras and those captured by UAVs. Person Re-ID methods based on ground person images have difficulty when performing Re-ID based on aerial person images. In this paper, we first propose a novel meta-transfer learning method for person Re-ID in aerial imagery; this approach trains a generalisable Re-ID model to learn discriminative feature representations for aerial person images. Specifically, a meta-learning strategy is introduced to study a feature extractor, and a transfer learning strategy is introduced to utilise and further improve the acquired meta-knowledge. To overcome the convergence speed and recognition accuracy reductions caused by the presence of difficult categories in the given dataset, we propose a learning strategy based on curriculum sampling that is harmonised with our meta-transfer learning framework. In addition, a new metric formulation of sample similarity is introduced based on Mahalanobis distance to improve the optimisation of the model. Extensive comparative evaluation experiments are conducted on the large-scale aerial Re-ID dataset, and the results obtained show that our method achieves a Rank-1 accuracy of 63.63 $$\%$$ and a mean average precision (mAP) of 38.02 $$\%$$ , demonstrating its potential for completing person Re-ID in aerial images. Ablation studies also validate that each component contributes to improving the performance of the model.

Existing person re-identification methods rely on the visual sensor to capture the pedestrians. The image or video data from visual sensor inevitably suffers the occlusion and dramatic variations of pedestrian postures, which degrades the re-identification performance and further limits its application to the open environment. On the other hand, for most people, one of the most important carry-on items is the mobile phone, which can be sensed by WiFi and cellular networks in the form of a wireless positioning signal. Such signal is robust to the pedestrian occlusion and visual appearance change, but suffers some positioning error. In this work, we approach person re-identification with the sensing data from both vision and wireless positioning. To take advantage of such cross-modality cues, we propose a novel recurrent context propagation module that enables information to propagate between visual data and wireless positioning data and finally improves the matching accuracy. To evaluate our approach, we contribute a new Wireless Positioning Person Re-identification (WP-ReID) dataset. Extensive experiments are conducted and demonstrate the effectiveness of the proposed algorithm. Code will be released at https://github.com/yolomax/WP-ReID.

Prevalent nighttime person re-identification (ReID) methods typically combine image relighting and ReID networks in a sequential manner. However, their performance (recognition accuracy) is limited by the quality of relighting images and insufficient collaboration between image relighting and ReID tasks. To handle these problems, we propose a novel Collaborative Enhancement Network called CENet, which performs the multilevel feature interactions in a parallel framework, for nighttime person ReID. In particular, the designed parallel structure of CENet can not only avoid the impact of the quality of relighting images on ReID performance, but also allow us to mine the collaborative relations between image relighting and person ReID tasks. To this end, we integrate the multilevel feature interactions in CENet, where we first share the Transformer encoder to build the low-level feature interaction, and then perform the feature distillation that transfers the high-level features from image relighting to ReID, thereby alleviating the severe image degradation issue caused by the nighttime scenario while avoiding the impact of relighting images. In addition, the sizes of existing real-world nighttime person ReID datasets are limited, and large-scale synthetic ones exhibit substantial domain gaps with real-world data. To leverage both small-scale real-world and large-scale synthetic training data, we develop a multi-domain learning algorithm, which alternately utilizes both kinds of data to reduce the inter-domain difference in training procedure. Extensive experiments on two real nighttime datasets, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Night600</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RGBNT201_rgb</i> , and a synthetic nighttime ReID dataset are conducted to validate the effectiveness of CENet. We release the code and synthetic dataset at: https://github.com/Alexadlu/CENet.

Person Re-ID is a challenging problem and is gaining more attention due to demands in security, intelligent system and other applications. Most person Re-ID works are vision-based, such as image, video, or broadly speaking, face recognition-based techniques. Recently, several multi-modal person Re-ID datasets were released, including RGB+IR, RGB+text, RGB+WiFi, which shows the potential of the multi-modal sensor-based person Re-ID approach. However, there are several common issues in public datasets, such as short time duration, lack of appearance change, and limited activities, resulting in un-robust models. For example, vision-based Re-ID models are sensitive to appearance change. In this work, a person Re-ID testbed with multi-modal sensors is created, allowing the collection of sensing modalities including RGB, IR, depth, WiFi, radar, and audio. This novel dataset will cover normal daily office activities with large time span over multi-seasons. Initial analytic results are obtained for evaluating different person Re-ID models, based on small datasets collected in this testbed.

To fully pay attention to identity-sensitive feature information and utilize the correlations of inter-attributes and attributes-body parts, this paper proposes a person re-identification (re-ID) method based on the construction of graph convolutional network (GCN) with crucial attribute feature and body parts. First, it establishes the multiscale context-aware network (MSCAN) using dilated convolution with different expansion ratios, which can learn multiscale context information and obtain diversified global features. Subsequently, the human parsing model is utilized to extract the body part features. According to the attribute importance degree, the paper constructs low-dimensional GCN integrating the vital attributes and body parts of person descriptions to obtain discriminative local features. Finally, based on attribute prediction, it reduces the range of the images to be matched with discriminating possible objects from query images, thereby simplifying retrieval process. The experimental results demonstrate that the novel designed method can effectively improve person re-ID performance and achieve competitive evaluation results on typical public testing datasets.

Person re-identification plays important roles in many practical applications. Due to various human poses, complex backgrounds and similarity of person clothes, person re-identification is still a challenging task. In this paper, we mainly focus on the robust and discriminative appearance feature representation and proposed a novel multi-appearance method for person re-identification. First, we proposed a deep feature fusion method and get the multi-appearance feature by combining two Convolutional Neural Networks. Then, in order to further enhance the representation of the appearance feature, the multi-part model was constructed by combining the whole body and the six body parts. Additionally, we optimized the feature extraction process by adding a pooling layer. Comprehensive and comparative experiments with the state-of-the-art methods over publicly available datasets demonstrated that the proposed method can get promising results.

Unsupervised Domain Adaptation (UDA) methods for person Re-Identification (Re-ID) rely on target domain samples to model the marginal distribution of the data. To deal with the lack of target domain labels, UDA methods leverage information from labeled source samples and unlabeled target samples. A promising approach relies on the use of unsupervised learning as part of the pipeline, such as clustering methods. The quality of the clusters clearly plays a major role in methods performance, but this point has been overlooked. In this work, we propose a multi-step pseudo-label refinement method to select the best possible clusters and keep improving them so that these clusters become closer to the class divisions without knowledge of the class labels. Our refinement method includes a cluster selection strategy and a camera-based normalization method which reduces the within-domain variations caused by the use of multiple cameras in person Re-ID. This allows our method to reach state-of-the-art UDA results on DukeMTMC-Market1501 (source-target). We surpass state-of-the-art for UDA Re-ID by 3.4% on Market1501-DukeMTMC datasets, which is a more challenging adaptation setup because the target domain (DukeMTMC) has eight distinct cameras. Furthermore, the camera-based normalization method causes a significant reduction in the number of iterations required for training convergence.

This paper proposes a method for video-based person re-identification. Motivated by the capacity of Recurrent Feature Aggregation Network (RFA-Net) that allows to aggregate the image-level features over time-steps and to yield a sequence level feature. Our work improves the person re- identification performance by (1) proposing to use Gaussian of Gaussian (GOG) - a more powerful feature at image level instead of LBP-Color as in the original work and (2) applying metric learning techniques for person matching. To analyze the role of image feature and metric learning, we have performed extensive experiments on two public benchmark datasets including PRID 2011 and iLIDS- VID. The proposed solution obtains impressive results on the PRID 2011 dataset. The matching rate at rank 1 is increased by 16.8% compared to that of original method. For the iLID-SVID dataset, the matching rate at rank 1 of proposed method is increased 1.1% whereas these values at the latter ranks are higher than those of the original method by approximately 4%. In additional, the mean and standard deviation of the rank where the correct matching is found of the proposed method on the PRID 2011 and the iLIDS-VID are 2.32, 4.33 and 4.61, 7.67 while those of the original work are 3.50, 5.42 and 8.03, 17.52, respectively. These obtained results emphasize the robustness of the proposed method compared to the original one.

In recent years, considerable progress has been witnessed in the person re-identification (Re-ID). However, in a more realistic long-term scenario, the appearance shift arising from the clothes-changing inevitably deteriorates the conventional methods that heavily depend on the clothing color. Although the current clothes-changing person Re-ID methods introduce external human knowledge (i.e, contour, mask) and sophisticated feature decoupling strategy to alleviate the clothing shift, they still face the risk of overfitting to clothing due to the limited clothing diversity of training set. To more efficiently and effectively promote the clothes-irrelevant feature learning, we present a novel joint Identity-aware Mixstyle and Graph-enhanced Prototype method for clothes-changing person Re-ID. Specifically, by treating the cloth-changing as fine-grained domain/style shift, the identity-aware mixstyle (IMS) is proposed from the perspective of domain generalization, which mixes the instance-level feature statistics of samples within each identity to synthesize novel and diverse clothing styles, while retaining the correspondence between synthesized samples and latent label space. By incorporating the IMS module, the more diverse styles can be exploited to train a clothing-shift robust model. To further reduce the feature discrepancy caused by clothing variations, the graph-enhanced prototype constraint (GEP) module is proposed to explore the graph similarity structure of style-augmented samples across memory bank to build informative and robust prototypes, which serve as powerful exemplars for better clothing-irrelevant metric learning. The two modules are integrated into a joint learning framework and benefit each other. The extensive experiments conducted on clothes-changing person Re-ID datasets validate the superiority and effectiveness of our method. In addition, our method also shows good universality and corruption robustness on other Re-ID tasks.

Deep learning-based person re-identification methods: A survey and outlook of recent works

Person re-identification is an important issue in the field of video surveillance security. However, the differences of pedestrians under different cameras bring challenges. The existing person re-identification methods mainly focus on the person's posture and feature extraction, but do not pay much attention to the high-frequency clothes changing for one person. Therefore, the study of person clothes-changing images is of great help to person re-identification. In this paper, we introduce a network which fuses feature separation and self-attention jointly in a generative adversarial network for image generation of person clothes. We evaluate this model based on self-attention on a benchmark dataset: Market-1501. The experiment results validate the advances of attention mechanism by comparing with state-of-the-art baselines. Based on the generated datasets, we introduce a network containing channel attention and spatial attention which focus on local regions of clothes changes in a convolutional neural network for image recognition. We evaluate this network on our generative dataset. The experiment results show that joint attention network outperforms the baseline.

Researches on person re-identification have become more prevalent as a core technique in visual surveillance systems. While a large volume of person re-identification (Re-ID) methods adopt experimental settings with a large patch size, relatively small ones are not much considered. Thus, this work focuses on investigating comparison of person Re-ID performance when color and shape features-based are adopted for small patch size images with low resolution. First, the deep decompositional network is used to divide the person into upper and lower body parts. Then, color and shape features are extracted. Finally, using single or combination of features, similarity-based ranked matching scores are computed. The person Re-ID performance is evaluated based on VIPeR dataset. From the experiment results, we found that color-based feature is better than shape-based features, and the combination of color and shape features-based can be meaningful.

Most existing person re-identification (re-id) methods are designed based on the artificial closed-set assumption that the probe and gallery identities are exactly overlapped with a small search pool. This leads to poor scalability in real-world applications where the task is often to re-id a small set of target people (i.e., watch-list) among a large search pool with unknown ID overlap, namely, an open-set deployment setting. In this paper, we firstly propose a new person re-id setting called Watch-List based Open-Set (WLOS) person re-id, which is characterised by the above open-set deployment and a watch-list available at the training stage. Then, we address such a under-studied WLOS problem by formulating a novel Task Dedicated Deep Hashing (TDDH) approach which learning a purpose-specific deep hash model particularly for the given target people in an efficient end-to-end manner. Extensive experiments on three large-scale re-id benchmarks are conducted to demonstrate the advantages and superiority of the TDDH over a wide range of the state-of-the-art hashing and re-id methods under the more realistic open-set setting.

Person re-identification (re-ID) is a promising computer vision task. State-of-the-art methods mainly utilize deep learning based approaches to learn visual features for describing person appearances. Due to occlusion, complex background, different postures and light intensity, the technology faces many challenges. In this paper, a person re-identification method is proposed combining mask guiding and part pooling. First, person mask is generated by a segmentation sub-net, combining Macro-Micro Adversarial Network (MMAN) with Fully Convolution Networks (FCN). To alleviate the influence of background, a mask guiding strategy is designed integrating the mask with person feature map. Then a part pooling strategy is employed to extract local features of the person. The final loss function of the network is defined as the combination of global loss and local loss, which can describe the person in a comprehensive manner. Four public datasets are employed to test the performance of the proposed method. Our method achieves rank-1/mAP of 89.05%/72.83% on the Market-1501, 79.03%/62.06% on the DukeMTMC-reID, 46.79%/29.61% on the MSMT-17, 49.50%/45.03% on the CUHK03-NP. Experimental results show that the designed mask guiding and part pooling strategies can improve person re-ID performance.