DeepList: Learning Deep Features With Adaptive Listwise Constraint for Person Reidentification

Wang, Jin; Wang, Zheng; Gao, Changxin; Huang, Rui

doi:10.1109/tcsvt.2016.2586851

Cited by 59 publications

(23 citation statements)

References 38 publications

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“…The semi/un-supervised approaches include: SDALF [15], eSDC [100], t-LRDC [104], OSML [3], LSRO [105], CAMEL [94], UMDL [55], BoW [103] and PUL [14]. The supervised approaches include: DM 3 [78], DeepList [73], DDDM [79], Locally-Aligned [35], JointReid [1], SCSP [7], Multi-channel [11], DNSL [96], JSTL [90], SI-CI [70], S-CNN [68], SpindleNet [97], Part-Aligned [98], S-LSTM [69], E-Metric [62], Deep-Embed [42], SSM [2], MSCAN [34], CADL [40], LADF [37], XQDA [39], OL-MANS [107], SalMatch [99] and PDC [64]. Note that not all the approaches above report the results of experiments in all three datasets.…”

Section: Comparison With State-of-the-artsmentioning

confidence: 99%

PAC-GAN: An effective pose augmentation scheme for unsupervised cross-view person re-identification

et al. 2020

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Person re-identification (person Re-Id) aims to retrieve the pedestrian images of a same person that captured by disjoint and non-overlapping cameras. Lots of researchers recently focuse on this hot issue and propose deep learning based methods to enhance the recognition rate in a supervised or unsupervised manner. However, two limitations that cannot be ignored: firstly, compared with other image retrieval benchmarks, the size of existing person Re-Id datasets are far from meeting the requirement, which cannot provide sufficient pedestrian samples for the training of deep model; secondly, the samples in existing datasets do not have sufficient human motions or postures coverage to provide more priori knowledges for learning. In this paper, we introduce a novel unsupervised pose augmentation cross-view person Re-Id scheme called PAC-GAN to overcome these limitations. We firstly present the formal definition of cross-view pose augmentation and then propose the framework of PAC-GAN that is a novel conditional generative adversarial network (CGAN) based approach to improve the performance of unsupervised corss-view person Re-Id. Specifically, The pose generation model in PAC-GAN called CPG-Net is to generate enough quantity of pose-rich samples from original image and skeleton samples. The pose augmentation dataset is produced by combining the synthesized poserich samples with the original samples, which is fed into the corss-view person Re-Id model named Cross-GAN. Besides, we use weight-sharing strategy in the CPG-Net to improve the quality of new generated samples. To the best of our knowledge, we are the first try to enhance the unsupervised cross-view person Re-Id by pose augmentation, and the results of extensive experiments show that the proposed scheme can combat the state-of-the-arts.

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Section: Comparison With State-of-the-artsmentioning

confidence: 99%

PAC-GAN: An effective pose augmentation scheme for unsupervised cross-view person re-identification

et al. 2020

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“…Person re-id has attracted much attention in recent years, and we first point the readers to some literature surveys on this topic [3,5,6,7,33]. Many methods focus on tackling this problem, which can be roughly divided into three categories, i.e., feature representation [8,34,35], metric learning [36,37,38,39] and deep learning [40,41,42,43,44,45,46,47,48]. Since this paper focuses on the video-based re-id task, this section only gives a review of the literature closely related to this work.…”

Section: Related Workmentioning

confidence: 99%

Superpixel-Based Temporally Aligned Representation for Video-Based Person Re-Identification

Gao

Wang

Liu

et al. 2019

Sensors

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Most existing person re-identification methods focus on matching still person images across non-overlapping camera views. Despite their excellent performance in some circumstances, these methods still suffer from occlusion and the changes of pose, viewpoint or lighting. Video-based re-id is a natural way to overcome these problems, by exploiting space–time information from videos. One of the most challenging problems in video-based person re-identification is temporal alignment, in addition to spatial alignment. To address the problem, we propose an effective superpixel-based temporally aligned representation for video-based person re-identification, which represents a video sequence only using one walking cycle. Particularly, we first build a candidate set of walking cycles by extracting motion information at superpixel level, which is more robust than that at the pixel level. Then, from the candidate set, we propose an effective criterion to select the walking cycle most matching the intrinsic periodicity property of walking persons. Finally, we propose a temporally aligned pooling scheme to describe the video data in the selected walking cycle. In addition, to characterize the individual still images in the cycle, we propose a superpixel-based representation to improve spatial alignment. Extensive experimental results on three public datasets demonstrate the effectiveness of the proposed method compared with the state-of-the-art approaches.

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“…Since person re-identification could be considered as a retrieval problem based on ranking, some person re-identification approaches applied these techniques like Prosser et al [10] who reformulated the person re-identification problem as a ranking problem and learn a set of weak RankSVMs, each computed on a small set of data then combine them to build a stronger ranker using ensemble learning. Wang et al [11] applied the ListMLE method to the person re-identification problem: they map a list of similarity scores to a probability distribution, then utilize the negative log likelihood of ground truth permutations as the loss function.…”

Section: Related Workmentioning

confidence: 99%

Similarity Learning with Listwise Ranking for Person Re-Identification

Chen

Duffner

Başkurt

et al. 2018

2018 25th IEEE International Conference on Image Processing (ICIP)

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Person re-identification is an important task in video surveillance systems. It consists in matching an image of a probe person among a gallery image set of people detected from a network of surveillance cameras with non-overlapping fields of view. The main challenge of person re-identification is to find image representations that are discriminating the persons' identities and that are robust to the viewpoint, body pose, illumination changes and partial occlusions. In this paper, we proposed a metric learning approach based on a deep neural network using a novel loss function which we call the Rank-Triplet loss. This proposed loss function is based on the predicted and ground truth ranking of a list of instances instead of pairs or triplets and takes into account the improvement of evaluation measures during training. Through our experiments on two person re-identification datasets, we show that the new loss outperforms other common loss functions and that our approach achieves state-of-the-art results on these two datasets.

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DeepList: Learning Deep Features With Adaptive Listwise Constraint for Person Reidentification

Cited by 59 publications

References 38 publications

PAC-GAN: An effective pose augmentation scheme for unsupervised cross-view person re-identification

PAC-GAN: An effective pose augmentation scheme for unsupervised cross-view person re-identification

Superpixel-Based Temporally Aligned Representation for Video-Based Person Re-Identification

Similarity Learning with Listwise Ranking for Person Re-Identification

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