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Conference papers
Progressive Learning With Anchoring Regularization For Vehicle Re-Identification
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.
https://hal-univ-evry.archives-ouvertes.fr/hal-03606607
Contributor : Frédéric Davesne Connect in order to contact the contributor
Submitted on : Friday, March 11, 2022 - 9:38:26 PM
Last modification on : Sunday, March 13, 2022 - 3:22:01 AM
Contributor : Frédéric Davesne Connect in order to contact the contributor
Submitted on : Friday, March 11, 2022 - 9:38:26 PM
Last modification on : Sunday, March 13, 2022 - 3:22:01 AM
