Market-1501 Datasets Research Articles

EcReID: Enhancing Correlations from Skeleton for Occluded Person Re-Identification

Person re-identification is a challenging task due to the lack of person image information in occluded scenarios. The current methods for person re-identification only take into account global information, neglect local information, and are not responsive to changes in input. Additionally, these methods do not address the issue of inaccurate joint detection caused by occlusion. In this paper, we propose an occluded person re-identification method based on a graph model and deformable method, which is able to simultaneously focus on global and local information and can flexibly adapt to local information and changes in the input, efficiently resolving issues such as occluded or incorrect joint information. Our method consists of three modules: the mutual help denoising module, inter-node aggregation and update module, and graph matching module. The mutual help denoising module acquires global features and person skeleton node features using a CNN backbone network and a pose estimation model, respectively. It uses symmetric deformable graph attention to obtain the local and global features of the joint points in different views, correcting the information of incorrect nodes and extracting favorable human features. The inter-node aggregation and update module employs deformable graph convolution operations to enhance the relations between the nodes in the same view, resulting in higher-order information. The graph matching module uses graph matching methods based on the human topology to obtain a more accurate similarity calculation for masked images. Experimental results on the Occluded-Duck and Occluded-REID datasets show that our proposed method achieves Rank-1 accuracies of 64.8% and 84.5%, respectively, outperforming current mainstream methods such as HOReID. Our method also achieves good results on the MARKET-1501 and DukeMTMC-ReID datasets. These results demonstrate that our proposed method can extract person features well and effectively improve the accuracy of person re-identification tasks.

Semi-supervised LDA pedestrian re-identification algorithm based on K-nearest neighbor resampling

Person re-identification identify a specific person in surveillance network by similarity measurement between images of different camera views. However, existing metric learning based methods suffer from over-fitting problem. To solve this problem, a resampled linear discriminant analysis (LDA) method was proposed based on the statistical and topological characteristics of pedestrian images. This method utilized the k-nearest neighbours to form potential positive sample pairs. The potential positive pairs are used to improve the metric model and generalize the metric model to the test data. By minimizing the inter-class divergence of potential positive sample pairs, a semi-supervised re-sampling LDA person re-identification algorithm was established. It was then tested on the VIPeR, CUHK01 and Market 1501datasets. The results show that the proposed method achieves the best performance compared to some available methods. Especially, the proposed method outplays the best comparison method by 0.6% and 5.76% at rank-1 identification rate on the VIPeR and CUHK01 datasets respectively. At the same time, the improved LDA algorithm has improved the rank-1 identification accuracy of traditional LDA method by 9.36% and 32.11% on these two datasets respectively. Besides, the proposed method is limited to Market-1501 dataset when the test data is of large size.

A Multi-Attention Approach for Person Re-Identification Using Deep Learning

Person re-identification (Re-ID) is a method for identifying the same individual via several non-interfering cameras. Person Re-ID has been felicitously applied to an assortment of computer vision applications. Due to the emergence of deep learning algorithms, person Re-ID techniques, which often involve the attention module, have gained remarkable success. Moreover, people's traits are mostly similar, which makes distinguishing between them complicated. This paper presents a novel approach for person Re-ID, by introducing a multi-part feature network, that combines the position attention module (PAM) and the efficient channel attention (ECA). The goal is to enhance the accuracy and robustness of person Re-ID methods through the use of attention mechanisms. The proposed multi-part feature network employs the PAM to extract robust and discriminative features by utilizing channel, spatial, and temporal context information. The PAM learns the spatial interdependencies of features and extracts a greater variety of contextual information from local elements, hence enhancing their capacity for representation. The ECA captures local cross-channel interaction and reduces the model's complexity, while maintaining accuracy. Inclusive experiments were executed on three publicly available person Re-ID datasets: Market-1501, DukeMTMC, and CUHK-03. The outcomes reveal that the suggested method outperforms existing state-of-the-art methods, and the rank-1 accuracy can achieve 95.93%, 89.77%, and 73.21% in trials on the public datasets Market-1501, DukeMTMC-reID, and CUHK03, respectively, and can reach 96.41%, 94.08%, and 91.21% after re-ranking. The proposed method demonstrates a high generalization capability and improves both quantitative and qualitative performance. Finally, the proposed multi-part feature network, with the combination of PAM and ECA, offers a promising solution for person Re-ID, by combining the benefits of temporal, spatial, and channel information. The results of this study evidence the effectiveness and potential of the suggested method for person Re-ID in computer vision applications.

Hybrid partial-constrained learning with orthogonality regularization for unsupervised person re-identification

Person re-identification (re-ID) aims at determining whether there is a specific person in image sets or videos via computer vision technology. State-of-the-art unsupervised re-ID methods extract image features through CNNs-based networks and store these extracted features in memory for identity matching. However, extracted global features of these methods ignore the problem of information redundancy and the influence of the constraints between the internal features. To overcome these problems, a Hybrid Partial-constrained Learning (HPcL) network with orthogonality regularization is proposed to learn a discriminative visual representation by generating hybrid features. Specifically, the hybrid features are generated by our designed Dynamic Fusion Module (DFM) to initialize the memory dictionary and match the identity, which can constrain each part of the features extracted by our proposed Multi-Scale (M-S) module and learn robust visual representations. In addition, a new orthogonal regularization method is introduced to constrain orthogonality of the kernel weights and features, which reduces the correlations among features. Extensive experimental results on Market-1501, DukeMTMC-reID, PersonX, and MSMT17 datasets demonstrate that our method is effective and superior to the state-of-the-art methods.

Lightweight pyramid residual features with attention for person re-identification

Person re-identification is one of the problems in the computer vision field that aims to retrieve similar human images in some image collections (or galleries). It is very useful for people searching or tracking in a closed environment (like a mall or building). One of the highlighted things on person re-identification problems is that the model is usually designed only for performance instead of performance and computing power consideration, which is applicable for devices with limited computing power. In this paper, we proposed a lightweight residual network with pyramid attention for person re-identification problems. The lightweight residual network adopted from the residual network (ResNet) model used for CIFAR dataset experiments consists of not more than two million parameters. An additional pyramid features extraction network and attention module are added to the network to improve the classifier's performance. We use CPFE (Context-aware Pyramid Features Extraction) network that utilizes atrous convolution with different dilation rates to extract the pyramid features. In addition, two different attention networks are used for the classifier: channel-wise and spatial-based attention networks. The proposed classifier is tested using widely use Market-1501 and DukeMTMC-reID person re-identification datasets. Experiments on Market-1501 and DukeMTMC-reID datasets show that our proposed classifier can perform well and outperform the classifier without CPFE and attention networks. Further investigation and ablation study shows that our proposed classifier has higher information density compared with other person re-identification methods.

Body Correlation Network for Person Re-Identifications

<p>Visual information accounts for approximately 80 to 85 percent of the information available daily in modern cities. As such, person re-identification, an instance-level image retrieval task, has become an important research topic in computer vision, machine learning, and other fields in recent years. Traditional person re-identification methods based on convolutional neural networks only extract the global feature information of people. Thus, when external factors such as changes in occlusion and illumination disturb people, the recognition performance of these methods substantially decreases. We therefore develop body correlation network (BC-Net), which takes full advantage of images of body parts and the correlations between them. Specifically, BC-Net uses body part feature information and correlation feature information as nodes and edges, respectively, and then uses graph convolutions to learn the overall topology of people. To improve use of crucial feature information, we also design a unique method of propagation between nodes and edges. We conduct extensive comparative experiments on the Market-1501 and DukeMTMC-reID datasets, and the results demonstrate that BC-Net outperforms other state-of-the-art techniques.</p> <p> </p>

Combining Human Parsing with Analytical Feature Extraction and Ranking Schemes for High-Generalization Person Reidentification

Person reidentification (re-ID) has been receiving increasing attention in recent years due to its importance for both science and society. Machine learning (particularly Deep Learning (DL)) has become the main re-ID tool that has allowed to achieve unprecedented accuracy levels on benchmark datasets. However, there is a known problem of poor generalization in respect of DL models. That is, models that are trained to achieve high accuracy on one dataset perform poorly on other ones and require re-training. In order to address this issue, we present a model without trainable parameters. This, in turn, results in a great potential for high generalization. This approach combines a fully analytical feature extraction and similarity ranking scheme with DL-based human parsing wherein human parsing is used to obtain the initial subregion classification. We show that such combination, to a high extent, eliminates the drawbacks of existing analytical methods. In addition, we use interpretable color and texture features that have human-readable similarity measures associated with them. In order to verify the proposed method we conduct experiments on Market1501 and CUHK03 datasets, thus achieving a competitive rank-1 accuracy comparable with that of DL models. Most importantly, we show that our method achieves 63.9% and 93.5% rank-1 cross-domain accuracy when applied to transfer learning tasks, while also being completely re-ID dataset agnostic. We also achieve a cross-domain mean average precision (mAP) that is higher than that of DL models in some experiments. Finally, we discuss the potential ways of adding new features to further improve the model. We also show the advantages of interpretable features for the purposes of constructing human-generated queries from verbal descriptions in order to conduct searches without a query image.

A novel Multi-scale architecture driven by decoupled semantic attention transfer for person image generation

Person image generation is a challenging task aimed to transfer the person of the source image from a source pose to a target pose while preserving its style. In this paper, we proposed a Generative Adversarial Network based on Decoupled Semantic Attention Transfer (DSAT-GAN), focusing on that local semantic representations of different image styles and contents cannot be accurately decoupled and transferred. This architecture employs a novel Multi-scale Semantic Mapping Generation Network (Ms-SMGN), driven by two network modules with different semantic attention mechanism, aiming to accurately align and transfer the representations of local semantics at different spatial scales. Then, a channel-separated convolution is applied in the encoding networks instead of the traditional channel fully-connected operation, which reduces computational complexity while realizing channel semantic decoupling. Moreover, a Gram matrix-based global style loss is introduced to further enhance the consistency of high-level semantic between generated and target images. Experiments on Market-1501 and DeepFashion datasets show that DSAT-GAN has superior performance compared with other recent baselines. Additionally, this architecture can be extended to the data enhancement scenes to significantly improve the accuracy of person Re-identification.

Person Re-Identification Based on Contour Information Embedding.

Person re-identification (Re-ID) plays an important role in the search for missing people and the tracking of suspects. Person re-identification based on deep learning has made great progress in recent years, and the application of the pedestrian contour feature has also received attention. In the study, we found that pedestrian contour feature is not enough in the representation of CNN. On this basis, in order to improve the recognition performance of Re-ID network, we propose a contour information extraction module (CIEM) and a contour information embedding method, so that the network can focus on more contour information. Our method is competitive in experimental data; the mAP of the dataset Market1501 reached 83.8% and Rank-1 reached 95.1%. The mAP of the DukeMTMC-reID dataset reached 73.5% and Rank-1 reached 86.8%. The experimental results show that adding contour information to the network can improve the recognition rate, and good contour features play an important role in Re-ID research.

Diff attention: A novel attention scheme for person re-identification

The Person Re-ID has made great progress benefiting from the advance of deep neural networks and the attention mechanism in recent years. However, existing models and attentions for person Re-ID only focus on learning the robust features while neglecting the difference between features of pairs, which is the core of feature similarity matching tasks. To address this problem, we propose the Diff Attention idea and design Diff Attention Module to guide the network to learn a more discriminative attention map based on the difference of feature vectors. Taking the Diff Attention Module, we develop Diff Attention Framework to match various backbone Re-ID models and train them. To efficiently train Diff Attention Framework, we also propose two training strategies: Joint Training and Separate Training. Our framework and strategies have achieved excellent performance on Market-1501, CUHK03, and MSMT17 datasets. Our code will be made publicly available at https://github.com/linxin98/ReID-DiffAttention.

A Real-Time Memory Updating Strategy for Unsupervised Person Re-Identification

Recently, clustering-based methods have been the dominant solution for unsupervised person re-identification (ReID). Memory-based contrastive learning is widely used for its effectiveness in unsupervised representation learning. However, we find that the inaccurate cluster proxies and the momentum updating strategy do harm to the contrastive learning system. In this paper, we propose a real-time memory updating strategy (RTMem) to update the cluster centroid with a randomly sampled instance feature in the current mini-batch without momentum. Compared to the method that calculates the mean feature vectors as the cluster centroid and updating it with momentum, RTMem enables the features to be up-to-date for each cluster. Based on RTMem, we propose two contrastive losses, i.e., sample-to-instance and sample-to-cluster, to align the relationships between samples to each cluster and to all outliers not belonging to any other clusters. On the one hand, sample-to-instance loss explores the sample relationships of the whole dataset to enhance the capability of density-based clustering algorithm, which relies on similarity measurement for the instance-level images. On the other hand, with pseudo-labels generated by the density-based clustering algorithm, sample-to-cluster loss enforces the sample to be close to its cluster proxy while being far from other proxies. With the simple RTMem contrastive learning strategy, the performance of the corresponding baseline is improved by 9.3% on Market-1501 dataset. Our method consistently outperforms state-of-the-art unsupervised learning person ReID methods on three benchmark datasets. Code is made available at:https://github.com/PRIS-CV/RTMem.

Effect of Reconstruction Losses in Discriminative and Generative Learning based Networks for the Person Re-identification

The Person Re-identification (Re-ID) task has gained popularity in recent times. Researchers are continuously looking to improve the accuracy of the existing person Re-ID systems. Identifying the person from the surveillance footage can be essential to security concerns. Currently, there are many state-of-art Person Re-ID systems available. Deep learning frameworks are also adopted for designing Re-ID systems. Apart from deep learning-based approaches, the Generative Adversarial Networks (GAN) based approach also gained substantial interest in Person Re-ID tasks. Augmentation of training data has significantly improved the performance of the system. Our primary objective is to analyze the effect of applying different reconstruction losses and their combinations on the GAN-based approach. The Discriminative and Generative Learning (DG-Net) approach is chosen for carrying out this study from other existing GAN-based systems. DG-Net is currently considered benchmarked in the GAN-based method for person Re-ID. Analysis shows that the proposed idea of using a variety of reconstruction losses simultaneously significantly improves the existing system's performance. Using the proposed technique of fusing multiple Losses simultaneously, we achieved a massive performance gain of 20.57% over the current benchmarked approach on the Market1501 dataset. This paper includes a thorough study of different loss functions and their effect on the generated images for performing Person Re-ID tasks.

Pedestrian re-identification based on attention mechanism and Multi-scale feature fusion.

Existing pedestrian re-identification models generally have low pedestrian retrieval accuracy when encountering factors such as changes in pedestrian posture and occlusion because the network cannot fully express pedestrian feature information. Therefore, this paper proposes a method to address this problem by combining the attention mechanism with multi-scale feature fusion, and combining the proposed cross-attention module with the ResNet50 backbone network. In this way, the ability of the network to extract strong salient features is significantly improved; at the same time, using the multi-scale feature fusion module to extract multi-scale features from different depths of the network, achieving the complementary advantages between features through feature addition, feature concatenation and feature weight selection. In addition, a feature enhancement method and an efficient pedestrian retrieval strategy are proposed to jointly promote the accuracy of pedestrian retrieval from both the training and testing levels. When tested on the occluded pedestrian recognition datasets Partial-REID and Partial-iLIDS, the accuracy of this method reached 70.1% and 65.6% on the Rank-1 indicator respectively, and 82.2% and 80.5% on the Rank-3 indicator respectively. At the same time, it also achieved high recognition accuracy when tested on the Market1501 dataset and DukeMTMC-reid dataset, reaching 95.9% and 89.9% on the Rank-1 indicator respectively, 89.1% and 80.3% on the mAP indicator respectively, and 67% and 46.2% on the mINP indicator respectively. It can be seen that this method has achieved good results in solving the above problems.

A Double Stream Person Re-Identification Method Based on Attention Mechanism and Multi-Scale Feature Fusion

Person re-identification is the key technology for designing a intelligent video surveillance system to retrieve and recongise a specific person in a series of videos. It mainly plays a role in multiple non-overlapping camera monitoring environments to determine whether the target person of interest that has appeared under a camera appears again under others. However, the image data, in the real scene, taken by the surveillance camera may be occluded or blurred, which increased the difficulty of identifying the pedestrian posturn and then lead to dramatically decrease the accuracy of recognition. To solve the above problems, we propose a dualbranch multi-scale feature fusion network, which improves the expression ability of pedestrian features under partial occlusion by learning discriminative pedestrian features. The feature erasure mechanism is introduced into the branch network to effectively enhance the fine-grained feature learning ability of the network. By embedding the lightweight attention module into Resnet-50, the image sequence features of the channel dimension will be extracted, and the interference caused by the cluttered back ground information will be suppressed. In the training phase, the average pooling layer and the maximum pooling layer of different kernels and strides are utilized for different residual stages, and the mixed pooling strategy and mixed loss function of different kernels are designed. Finally, a novel hybrid loss function which combines triplet loss and ID loss is presented to train the network model. By comparison with the existing representative methods on Market1501, DukeMTMC reID and MTMS17 datasets, the experimental results show that the features extracted by the proposed method are more discriminative and with high recognition accuracy.

Keypoint Aware Robust Representation for Transformer-Based Re-Identification of Occluded Person

Occluded person Re-identification is a challenging task which aims to find or distinguish a specific person when the human body is occluded by obstacles, other persons or oneself. Some recent state-of-the-art works adopting a transformer and/or pose-guided methods have improved the feature representation and performances, but are still in trouble with both weak representation and heavy structure. In this paper, we suggest the novel methods of transformer-based Re-identification for the occluded person as follows. First, in data augmentation, instead of deleting an arbitrary area, only a part containing the keypoint features of a person is deleted for effective learning in occlusion. Second, we suggest a unique hierarchical patch and feature based cross attention combining the reliability-enhanced heatmaps and the output of the transformer intermediate layer, which can more effectively pay attention to the non-occluded human region. Third, we propose an entire-partial loss function with non-similarity grouping for more robust feature representation. We compare our approach and various existing methods for the mAP and Rank-1 performances on the Occluded-Duke, Occluded-ReID, Market-1501 and DukeMTMC datasets. Experimental results demonstrate that our proposed model not only outperforms the existing methods, but also has the smallest scale among state-of-the-art methods.

Improved Method for Pedestrian Recognition Based on Generative Adversarial Networks

Traditional supervisory person re-id technology learning methods mainly relies on pre-marked image data, but there are a lot of unlabeled data in actual security scene, which seriously limits the application of person re-id technology in security monitoring field. Therefore, it is very important to study the semi-supervised learning of unlabeled data generated by antagonistic network. In the process of using GAN to generate data, in this paper we use global and local information of pedestrian image to generate realistic pedestrian image conditionally, and trains robust feature representations for different intra-class changes of cameras, so as to improve the accuracy of person re-id. The experimental results show that this method is more effective than the benchmark method. The performance of dataset Market1501 and Duke MTMC-reID improved by 4% and 3% respectively.

MPPM: A Mobile-Efficient Part Model for Object re-ID

Object re-identification (re-ID) is one of the core technologies in Multi-Object Tracking (MOT) that requires real-time decision-making. A Neural Processing Unit (NPU) is a low-power device that is dedicated to deploying neural network-based algorithms and has become one of the most important devices in today's mobile onboard systems. However, the current mainstream re-ID methods rarely consider the NPU characteristics, which makes it difficult for these methods to achieve both high onboard frame rates and high accuracies on an NPU. To address this problem, this paper focuses on designing a re-ID algorithm suitable for NPU deployment. The model of the object re-ID can be divided into two parts: the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">encoder</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">backbone</i> ) and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decoder</i> . In this article, a Mobile-efficient Pure Part Model (MPPM) is presented for re-ID task. First, for the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">backbone</i> of re-ID, we propose an efficient structure GogglesNet, which is composed of traditional convolutions. GogglesNet performs well on the re-ID task and can be comparable to lightweight networks on ImageNet with regard to accuracy and is faster on NPU. We then revisit the architectures of Pure Part Model (PPM) in person re-ID, including PCB and MGN, and propose a mobile-efficient <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decoder</i> Dual Pattern Network (DPN) for re-ID. The proposed MPPM achieves comparable performance with MGN on five re-ID datasets Market-1501, DukeMTMC-reID, MSMT17, VeRi-776, and VehicleID, while the proposed parameter amount is only 10.2% of it, and the speed on NPU is more than eight times higher.

Two-phase self-supervised pretraining for object re-identification

Self-supervised pretraining (e.g., MAE) based on reconstructing masked image patches has become increasingly popular in recent months. It commonly pretrains models on the large-scale ImageNet dataset with a considerably wider range of image categories, and then finetunes on downstream tasks such as classification, detection, segmentation, etc. However, we discover that for finetuning domain-specific tasks usually with a few or limited semantic spaces (e.g., object re-identification (ReID)), the model pretrained on a wider variety of sources (e.g., ImageNet) fails to be a good initialization. To address the problem, we propose a second-phase self-supervised pretraining on the specific domain, based on the self-supervised ImageNet pretraining models in the first phase. We perform studies on 2 domains of object ReID, i.e., person and vehicle ReID, where person ReID domain involves four datasets (Market-1501, DukeMTMC, MSMT17, Occluded-Duke) and vehicle ReID domain contains 2 datasets (VeRi-776, VehicleID). Through extensive experiments, we observe significant improvements in every single dataset based on the second-phase self-supervised pretraining over the entire unlabeled domain data. Specifically in person ReID task, compared to the self-supervised model in the first phase, our approach achieves +8.5%/+11.5%/+13.3%/+17.2% mAP improvements in Market-1501, DukeMTMC, MSMT17 and Occluded-Duke datasets, respectively. Furthermore, it also outperforms the supervised pretraining baselines, achieving improved performance by +1.5%/+4.3%/+3.9%/+4.2% mAP. We observe similar inspiring improvements in vehicle ReID tasks. Additionally, we also perform the second-phase pretraining on the specific tasks using its own unlabeled data, namely task-specific pretraining (TSP), which also consistently shows encouraging results across object ReID tasks.

Deep Learning-Based SOLO Architecture for Re-Identification of Single Persons by Locations

Analyzing and judging of captured and retrieved images of the targets from the surveillance video cameras for person re-identification have been a herculean task for computer vision that is worth further research. Hence, re-identification of single persons by locations based on single objects by locations (SOLO) model is proposed in this paper. To achieve the re-identification goal, we based the training of the re-identification model on synchronized stochastic gradient descent (SGD). SOLO is capable of exploiting the contextual cues and segmenting individual persons by their motions. The proposed approach consists of the following steps: (1) reformulating the person instance segmentation as: (a) prediction of category and (b) mask generation tasks for each person instance, (2) dividing the input person image into a uniform grids, i.e., G×G grid cells in such a way that a grid cell can predict the category of the semantic and masks of the person instances provided the center of the person falls into the grid cell and (3) conducting person segmentation. Discriminating features of individual persons are obtained by extraction using convolution neural networks. On person re-identification Market-1501 dataset, SOLO model achieved mAP of 84.1% and 93.8% rank-1 identification rate, higher than what is achieved by other comparative algorithms such as PL-Net, SegHAN, Siamese, GoogLeNet, and M 3 L (IBN-Net50). On person re-identification CUHK03 dataset, SOLO model achieved mAP of 82.1 % and 90.1% rank-1 identification rate, higher than what is achieved by other comparative algorithms such as PL-Net, SegHAN, Siamese, GoogLeNet, and M 3 L (IBN-Net50). These results show that SOLO model achieves best results for person re-identification, indicating high effectiveness of the model. The research contributions are: (1) Application of synchronized stochastic gradient descent (SGD) to SOLO training for person re-identification and (2) Single objects by locations using semantic category branch and instance mask branch instead of detect-then-segment method, thereby converting person instance segmentation into a solvable problem of single-shot classification.

Person Re-Identification with Improved Performance by Incorporating Focal Tversky Loss in AGW Baseline.

Person re-identification (re-ID) is one of the essential tasks for modern visual intelligent systems to identify a person from images or videos captured at different times, viewpoints, and spatial positions. In fact, it is easy to make an incorrect estimate for person re-ID in the presence of illumination change, low resolution, and pose differences. To provide a robust and accurate prediction, machine learning techniques are extensively used nowadays. However, learning-based approaches often face difficulties in data imbalance and distinguishing a person from others having strong appearance similarity. To improve the overall re-ID performance, false positives and false negatives should be part of the integral factors in the design of the loss function. In this work, we refine the well-known AGW baseline by incorporating a focal Tversky loss to address the data imbalance issue and facilitate the model to learn effectively from the hard examples. Experimental results show that the proposed re-ID method reaches rank-1 accuracy of 96.2% (with mAP: 94.5) and rank-1 accuracy of 93% (with mAP: 91.4) on Market1501 and DukeMTMC datasets, respectively, outperforming the state-of-the-art approaches.