Pixel and Feature Transfer Fusion for Unsupervised Cross-Dataset Person Reidentification

Yang, Yang; Wang, Guan’an; Tiwari, Prayag; Pandey, Hari Mohan; Lei, Zhen

doi:10.1109/tnnls.2021.3128269

Cited by 54 publications

(2 citation statements)

References 61 publications

(104 reference statements)

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“…Yang et al, [17] proposed recurrent auto encoder (RAE) framework to unify two approaches. The three modules of RAE are a feature-transfer (FT) module, a pixel-transfer (PT) module, and a Fusion module.…”

Section: Literature Surveymentioning

confidence: 99%

Image Based Person Re-Id Using GAN, DWT, and MLTP with ANN Classifier

2023

IJIES

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The recognition of an individual from images/videos captured from different cameras in real-world surveillance scenarios is the Person's re-identification. We propose image-based person reidentification (Re-id) using GAN, DWT, and MLTP with ANN classifiers. The central server database contains four benchmarked face databases by considering six images per person. The generative adversarial network (GAN) is used to convert side-angled test images into frontal images for an effective person Re-id. The discrete wavelet transform (DWT) and modified local ternary pattern (MLTP) techniques are used on images of the central server database and generated frontal test images to extract initial features. The Final features are computed by using the convolution of the DWT-LL band and MLTP features. The artificial neural network (ANN) is used to categorize and recognize test images. The existing research based on HOG, Hu Invariant moments, CNN, and SVM is presented and published, having a recognition accuracy of 97%. However, our projected technique uses GAN, which improved recognition accuracy to 99% compared to existing techniques having low recognition accuracy.

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Section: Literature Surveymentioning

confidence: 99%

Image Based Person Re-Id Using GAN, DWT, and MLTP with ANN Classifier

2023

IJIES

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“…In person re-id [17]- [20], both white-box and black box attacks have been proposed in [21]- [24]. These attacks use a labeled source dataset and show that the attacks are transferable under cross-dataset or cross-model, or both settings.…”

Section: Introductionmentioning

confidence: 99%

Meta Generative Attack on Person Reidentification

Subramanyam¹

2023

Preprint

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Adversarial attacks have been recently investigated in person re-identification. These attacks perform well under cross dataset or cross model setting. However, the challenges present in cross-dataset cross-model scenario does not allow these models to achieve similar accuracy. To this end, we propose our method with the goal of achieving better transferability against different models and across datasets. We generate a mask to obtain better performance across models and use meta learning to boost the generalizability in the challenging cross-dataset crossmodel setting. Experiments on Market-1501, DukeMTMC-reID and MSMT-17 demonstrate favorable results compared to other attacks.

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Multi-scale local-global architecture for person re-identification

et al. 2022

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With the emergence of deep learning method, which has been driven a great success for the field of person re-identification (re-ID). However, the existing works mainly focus on first-order attention (i.e., spatial and channels attention) statistics to model the valuable information for person re-ID. On the other hand, most existing methods operate data points respectively, which ignores discriminative patterns to some extent. In this paper, we present an automated framework named multi-scale local-global for person re-ID. The framework consists of two components. The first component is that a high-order attention module is adopted to learn high-order attention patterns to model the subtle differences among pedestrians and to generate the informative attention features. On the other hand, a novel architecture named spectral feature transformation is designed to make for the optimization of group wise similarities. Furthermore, we fuse the components together to form an ensemble model for person re-ID. Extensive experiments were conducted on the three benchmark datasets, i.e., Market-1501, DukeMTMC-reID, CUHK03, showing the superiority of the proposed method.

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Pixel and Feature Transfer Fusion for Unsupervised Cross-Dataset Person Reidentification

Cited by 54 publications

References 61 publications

Image Based Person Re-Id Using GAN, DWT, and MLTP with ANN Classifier

Image Based Person Re-Id Using GAN, DWT, and MLTP with ANN Classifier

Meta Generative Attack on Person Reidentification

Multi-scale local-global architecture for person re-identification

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