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Conference Papers Year : 2021

Progressive Learning With Anchoring Regularization For Vehicle Re-Identification

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Abstract

Vehicle re-identification (re-ID) aims to automatically find vehicle identity from a large number of vehicle images captured from multiple cameras. Most existing vehicle re-ID approaches rely on fully supervised learning methodologies, where large amounts of annotated training data are required, which is an expensive task. In this paper, we focus our interest on semi-supervised vehicle re-ID, where each identity has a single labeled and multiple unlabeled samples in the training. We propose a framework which gradually labels vehicle images taken from surveillance cameras. Our framework is based on a deep Convolutional Neural Network (CNN), which is progressively learned using a feature anchoring regularization process. The experiments conducted on various publicly available datasets demonstrate the efficiency of our framework in re-ID tasks. Our approach with only 20% labeled data shows interesting performance compared to the state-of-the-art supervised methods trained on fully labeled data.
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Dates and versions

hal-03606607 , version 1 (11-03-2022)

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Mohamed Dhia Elhak Besbes, Hedi Tabia, Yousri Kessentini, Bassem Ben Hamed. Progressive Learning With Anchoring Regularization For Vehicle Re-Identification. 28th IEEE International Conference on Image Processing (ICIP 2021), Sep 2021, Anchorage,Al., United States. pp.1154--1158, ⟨10.1109/ICIP42928.2021.9506152⟩. ⟨hal-03606607⟩
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