GLAD: Global–Local-Alignment Descriptor for Scalable Person Re-Identification

Wei, Longhui; Zhang, Shiliang; Yao, Hantao; Gao, Wen; Tian, Qi

doi:10.1109/tmm.2018.2870522

Cited by 265 publications

(278 citation statements)

References 61 publications

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“…As shown in Table IV, we first compare our method with the related works on the Market-1501 and DukeMTMC-reID datasets. Some approaches that try to remove the influence from the backgrounds are included, such as human landmark detection method GLAD [2], segmentation method SPReID [5] and attention-based method HA-CNN [8]. Our approach achieves 95.0% rank-1 accuracy and 84.6% mAP accuracy on the Market-1501 dataset, 88.7% rank-1 accuracy and 77.0% mAP accuracy on the DukeMTMC-reID dataset.…”

Section: E Comparison With the State-of-the-art Methodsmentioning

confidence: 99%

“…By minimizing L, the proposed approach learns the foreground feature representations and the background feature representations simultaneously. Unlike existing works [1], [2], [3], [5], [7], [6], the prediction of the background and the training of person reidentification model are not separate. The addition of the target enhancement module and target attention loss makes the two branches couple and promote each other, which allows our model to obtain a more accurate separation of the foreground and background.…”

Section: The Overall Training Objectivementioning

confidence: 96%

“…To alleviate the adverse influence from the backgrounds, numerous methods [1], [2], [3], [4], [5], [6], [7], [8], [9] have been proposed. In [1], [2], [3], human landmark detectors are used to extract human keypoints and generate human part bounding boxes. In [5], [7], [6], segmentation models on pedestrian are applied to generate the whole body masks or multiple semantic regions.…”

Section: Introductionmentioning

confidence: 99%

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End-to-End Comparative Attention Networks for Person Re-Identification

Líu¹,

Feng²,

Qi³

et al. 2017

IEEE Trans. on Image Process.

547

317

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Abstract-Person re-identification across disjoint camera views has been widely applied in video surveillance yet it is still a challenging problem. One of the major challenges lies in the lack of spatial and temporal cues, which makes it difficult to deal with large variations of lighting conditions, viewing angles, body poses and occlusions. Recently, several deep learning based person re-identification approaches have been proposed and achieved remarkable performance. However, most of those approaches extract discriminative features from the whole frame at one glimpse without differentiating various parts of the persons to identify. It is essentially important to examine multiple highly discriminative local regions of the person images in details through multiple glimpses for dealing with the large appearance variance.In this paper, we propose a new soft attention based model, i.e., the end-to-end Comparative Attention Network (CAN), specifically tailored for the task of person re-identification. The end-to-end CAN learns to selectively focus on parts of pairs of person images after taking a few glimpses of them and adaptively comparing their appearance. The CAN model is able to learn which parts of images are relevant for discerning persons and automatically integrates information from different parts to determine whether a pair of images belongs to the same person. In other words, our proposed CAN model simulates the human perception process to verify whether two images are from the same person. Extensive experiments on four benchmark person re-identification datasets, including CUHK01, CHUHK03, Market-1501 and VIPeR, clearly demonstrate that our proposed end-to-end CAN for person re-identification outperforms well established baselines significantly and offer new state-of-the-art performance.

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Section: E Comparison With the State-of-the-art Methodsmentioning

confidence: 99%

Section: The Overall Training Objectivementioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

End-to-End Comparative Attention Networks for Person Re-Identification

Líu¹,

Feng²,

Qi³

et al. 2017

IEEE Trans. on Image Process.

547

317

View full text Add to dashboard Cite

show abstract

“…Su et al [20] proposed a Pose-driven Deep Convolutional (PDC) model to learn improved feature extraction and matching models from end-to-end. Wei et al [24] also adopted the human pose estimation, or key point detection approach, in his Global-Local-Alignment Descriptor (GLAD) algorithm. The local body parts are detected and learned together with the global image by the four-stream CNN model, which yields a discriminatory and robust representation.…”

Section: Aam Heatmapmentioning

confidence: 99%

AANet: Attribute Attention Network for Person Re-Identifications

Tay

Roy

Yap

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

314

155

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This paper proposes Attribute Attention Network (AANet), a new architecture that integrates person attributes and attribute attention maps into a classification framework to solve the person re-identification (re-ID) problem. Many person re-ID models typically employ semantic cues such as body parts or human pose to improve the re-ID performance. Attribute information, however, is often not utilized. The proposed AANet leverages on a baseline model that uses body parts and integrates the key attribute information in an unified learning framework. The AANet consists of a global person ID task, a part detection task and a crucial attribute detection task. By estimating the class responses of individual attributes and combining them to form the attribute attention map (AAM), a very strong discriminatory representation is constructed. The proposed AANet outperforms the best state-of-the-art method [22] using ResNet-50 by 3.36% in mAP and 3.12% in Rank-1 accuracy on DukeMTMC-reID dataset. On Market1501 dataset, AANet achieves 92.38% mAP and 95.10% Rank-1 accuracy with re-ranking, outperforming [13], another state of the art method using ResNet-152, by 1.42% in mAP and 0.47% in Rank-1 accuracy. In addition, AANet can perform person attribute prediction (e.g., gender, hair length, clothing length etc.), and localize the attributes in the query image.

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“…These points can then be used to define the bounding boxes of limbs. GLAD [13] takes advantage of the DeeperCut [14] pose estimation method, which uses a 152-layer network to predict a series of skeleton key-points. It then uses a subset of these key-points to divide a person image into three areas -head, upper-body and lower-body.…”

Section: Introductionmentioning

confidence: 99%

Person reidentification using deep foreground appearance modeling

Watson

Bhalerao

2018

J. Electron. Imag.

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Person Re-Identification is the process of matching individuals from images taken of them at different times, and often with different cameras. To perform matching, most methods extract features from the entire image, however, this gives no consideration to the spatial context of the information present in the image. In this paper, we propose using a convolutional neural network approach based on ResNet-50 to predict the foreground of an image: the parts with the head, torso and limbs of a person. With this information, we use the LOMO and Salient Colour Name feature descriptors to extract features primarily from the foreground areas. In addition, we use a distance metric learning technique (XQDA), to calculate optimally weighted distances between the relevant features. We evaluate on the VIPeR, QMUL GRID and CUHK03 data sets, and compare our results against a linear foreground estimation method, and show competitive or better overall matching performance.

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GLAD: Global–Local-Alignment Descriptor for Scalable Person Re-Identification

Cited by 265 publications

References 61 publications

End-to-End Comparative Attention Networks for Person Re-Identification

End-to-End Comparative Attention Networks for Person Re-Identification

AANet: Attribute Attention Network for Person Re-Identifications

Person reidentification using deep foreground appearance modeling

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