Person re-identification by Local Maximal Occurrence representation and metric learning

Liao, Shengcai; Hu, Yang; Zhu, Xiangyu; Li, Stan Z.

doi:10.1109/cvpr.2015.7298832

Cited by 1,929 publications

(1,520 citation statements)

References 46 publications

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“…Following [Wang et al, 2016b], the evaluation is run on two simulated person datasets SALR-VIPeR and SALR-PRID, which are based on the VIPeR dataset [Gray et al, 2007] and the PRID450S dataset [Roth et al, 2014] respectively, and the public CAVIAR dataset [Cheng et al, 2011]. SALR-VIPeR.…”

Section: Experimental Datasets and Settingsmentioning

confidence: 99%

“…The PRID450S [Roth et al, 2014] is a challenge dataset, particularly there is camera characteristics variation. It contains 450 single shot image pairs captured over two spatially disjoint camera views.…”

Section: Experimental Datasets and Settingsmentioning

confidence: 99%

“…We choose ResNet-50 [He et al, 2016] as the base. The ResNet-50 is pre-trained with ImageNet [Russakovsky et al, 2015], and then fine-tuned with the Market-1501 [Zheng et al, 2015] dataset. (2) The cascaded generator networks are initialized with MSE losses.…”

Section: Implementation Detailsmentioning

confidence: 99%

“…mainly exploited [Ye et al, 2017;. In order to overcome the variations in illumination, occlusion and alignment, existing methods typically address the REID task by designing feature representation [Liao et al, 2015;Zhao et al, 2017] or learning distance metrics [Chen et al, 2015;Wang et al, 2017b;2017a;.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification

Wang

Yang

et al. 2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

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Person re-identification (REID) is an important task in video surveillance and forensics applications. Most of previous approaches are based on a key assumption that all person images have uniform and sufficiently high resolutions. Actually, various low-resolutions and scale mismatching always exist in open world REID. We name this kind of problem as Scale-Adaptive Low Resolution Person Re-identification (SALR-REID). The most intuitive way to address this problem is to increase various low-resolutions (not only low, but also with different scales) to a uniform high-resolution. SR-GAN is one of the most competitive image superresolution deep networks, designed with a fixed upscaling factor. However, it is still not suitable for SALR-REID task, which requires a network not only synthesizing high-resolution images with different upscaling factors, but also extracting discriminative image feature for judging person's identity. (1) To promote the ability of scale-adaptive upscaling, we cascade multiple SRGANs in series. (2) To supplement the ability of image feature representation, we plug-in a reidentification network. With a unified formulation, a Cascaded Super-Resolution GAN (CSR-GAN) framework is proposed. Extensive evaluations on two simulated datasets and one public dataset demonstrate the advantages of our method over related state-of-the-art methods.

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Section: Experimental Datasets and Settingsmentioning

confidence: 99%

Section: Experimental Datasets and Settingsmentioning

confidence: 99%

Section: Implementation Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification

Wang

Yang

et al. 2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

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show abstract

“…histograms [13] and a high dimensional color Warping (DTW) [31] , conditional random field (CRF) [30] , and top-push distance learning (TDL) Model [32] [35] .…”

Section: Scale Invariant Local Ternary Pattern (Siltp)mentioning

confidence: 99%

A Study on Person Re-Identification System using Enhanced RNN

Choi¹,

Xu²

2017

JIIBC

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The person Re-identification is the most challenging part of computer vision due to the significant changes in human pose and background clutter with occlusions. The picture from non-overlapping cameras enhance the difficulty to distinguish some person from the other. To reach a better performance match, most methods use feature selection and distance metrics separately to get discriminative representations and proper distance to describe the similarity between person and kind of ignoring some significant features. This situation has encouraged us to consider a novel method to deal with this problem. In this paper, we proposed an enhanced recurrent neural network with three-tier hierarchical network for person re-identification. Specifically, the proposed recurrent neural network (RNN) model contain an iterative expectation maximum (EM) algorithm and three-tier Hierarchical network to jointly learn both the discriminative features and metrics distance. The iterative EM algorithm can fully use of the feature extraction ability of convolutional neural network (CNN) which is in series before the RNN. By unsupervised learning, the EM framework can change the labels of the patches and train larger datasets. Through the three-tier hierarchical network, the convolutional neural network, recurrent network and pooling layer can jointly be a feature extractor to better train the network. The experimental result shows that comparing with other researchers' approaches in this field, this method also can get a competitive accuracy. The influence of different component of this method will be analyzed and evaluated in the future research.

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Self‐training with one‐shot stepwise learning method for person re‐identification

Xia

Liu

et al. 2021

Concurrency and Computation

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Summary Person re‐identification (Re‐ID) aims at identifying the same person across multiple non‐overlapping camera views. A number of existing methods have been presented for this task in a fully‐supervised manner that requires a large amount of training annotations. However, obtaining high quality labels is extremely time consuming and expensive. In this article, we focus on the semi‐supervised person Re‐ID and propose a one‐shot stepwise learning method to address the above issue. It exploits only one labeled data along with additional unlabeled samples to gradually but steadily improving the discriminative capability of the feature representation. Specifically, we first construct labeled data portion to train Re‐ID model. Then we fine‐tune the overall system by the following two steps iteratively: (1) assigning the estimated labels to the unlabeled portion; (2) updating the network parameters according to the selected data. During the propagation process, different from conventional sampling method, we propose a novel dynamic sampling strategy to enlarge the pseudo‐labeled subset step by step to make the pseudo labels more reliable. On Market‐1501, DukeMTMC‐ReID and MARS datasets, we conducted extensively experiments to demonstrate that our proposed method contributes indispensably and achieves a very competitive Re‐ID performance.

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Person re-identification by Local Maximal Occurrence representation and metric learning

Cited by 1,929 publications

References 46 publications

Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification

Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification

A Study on Person Re-Identification System using Enhanced RNN

Self‐training with one‐shot stepwise learning method for person re‐identification

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