Partition and Reunion: A Two-Branch Neural Network for Vehicle Re-identification

Hao Chen, Benoit Lagadec, Francois Bremond; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 184-192

Abstract


The smart city vision raises the prospect that cities will become more intelligent in various fields, such as more sustainable environment and a better quality of life for residents. As a key component of smart cities, intelligent transportation system highlights the importance of vehicle re-identification (Re-ID). However, as compared to the rapid progress on person Re-ID, vehicle Re-ID advances at a relatively slow pace. Some previous state-of-the-art approaches strongly rely on extra annotation, like attributes (e.g., vehicle color and type) and key-points (e.g., wheels and lamps). Recent work on person Re-ID shows that extracting more local features can achieve a better performance without considering extra annotation. In this paper, we propose an end-to-end trainable two-branch Partition and Reunion Network (PRN) for the challenging vehicle ReID task. Utilizing only identity labels, our proposed method outperforms existing state-of-the-art methods on four vehicle Re-ID benchmark datasets, including VeRi-776, VehicleID, VRIC and CityFlow-ReID by a large margin.

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[bibtex]
@InProceedings{Chen_2019_CVPR_Workshops,
author = {Chen, Hao and Lagadec, Benoit and Bremond, Francois},
title = {Partition and Reunion: A Two-Branch Neural Network for Vehicle Re-identification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}