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Progressive Learning With Anchoring Regularization For Vehicle Re-Identification | IEEE Conference Publication | IEEE Xplore

Progressive Learning With Anchoring Regularization For Vehicle Re-Identification

Publisher: IEEE

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 ...View more

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.
Date of Conference: 19-22 September 2021
Date Added to IEEE Xplore: 23 August 2021
ISBN Information:

ISSN Information:

Publisher: IEEE
Conference Location: Anchorage, AK, USA

References

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