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This work is part of the tasks related to the HVD (PID2021125051OB-I00) and the SEGA-CV (TED2021-131643A-I00) projects funded by the Ministerio de Ciencia e Innovacion of the Spanish Government.

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Vehicle Re-Identification Based on Unsupervised Domain Adaptation by Incremental Generation of Pseudo-Labels

Publicated to:ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2017, PT II. 14469 76-89 - 2024-01-01 14469(), DOI: 10.1007/978-3-031-49018-7_6

Authors: Moral, Paula; Garcia-Martin, Alvaro; Martinez, Jose M

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Abstract

The main goal of vehicle re-identification (ReID) is to associate the same vehicle identity in different cameras. This is a challenging task due to variations in light, viewpoints or occlusions; in particular, vehicles present a large intra-class variability and a small inter-class variability. In ReID, the samples in the test sets belong to identities that have not been seen during training. To reduce the domain gap between train and test sets, this work explores unsupervised domain adaptation (UDA) generating automatically pseudo-labels from the testing data, which are used to fine-tune the ReID models. Specifically, the pseudo-labels are obtained by clustering using different hyperparameters and incrementally due to retraining the model a number of times per hyperparameter with the generated pseudo-labels. The ReID system is evaluated in CityFlow-ReID-v2 dataset.

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