Cited By
View all- Wu JHong RTang S(2024)Intermediary-Generated Bridge Network for RGB-D Cross-Modal Re-IdentificationACM Transactions on Intelligent Systems and Technology10.1145/368206615:6(1-25)Online publication date: 29-Jul-2024
Neural Architectures for Feature Embedding in Person Re-Identification: A Comparative View
Article No.: 91, Pages 1 - 21
Abstract
Solving Person Re-Identification (Re-Id) through Deep Convolutional Neural Networks is a daunting challenge due to the small size and variety of the training data, especially in Single-Shot Re-Id, where only two images per person are available. The lack of training data causes the overfitting of the deep neural models, leading to degenerated performance.
This article explores a wide assortment of neural architectures that have been commonly used for object classification and analyzes their suitability in a Re-Id model. These architectures have been trained through a Triplet Model and evaluated over two challenging Single-Shot Re-Id datasets, PRID2011 and CUHK. This comparative study is aimed at obtaining the best-performing architectures and some concluding guidance to optimize the features embedding for the Re-Identification task. The obtained results present Inception-ResNet and DenseNet as potentially useful models, especially when compared with other methods, specifically designed for solving Re-Id.
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Index Terms
- Neural Architectures for Feature Embedding in Person Re-Identification: A Comparative View
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Publication History
Published: 09 October 2023
Online AM: 21 July 2023
Accepted: 13 July 2023
Revised: 24 May 2023
Received: 02 June 2021
Published in TIST Volume 14, Issue 5
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- Spanish Government through the CICYT projects
- Universidad Carlos III of Madrid through (PEAVAUTO-CMUC3M), and the Comunidad de Madrid through SEGVAUTO-4.0-CM
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View all- Wu JHong RTang S(2024)Intermediary-Generated Bridge Network for RGB-D Cross-Modal Re-IdentificationACM Transactions on Intelligent Systems and Technology10.1145/368206615:6(1-25)Online publication date: 29-Jul-2024
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