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Vehicle Re-Identification in Multi-Camera Scenarios Based on Ensembling Deep Learning Features
AbstractVehicle re-identification (ReID) across multiple cameras is one of the principal issues in Intelligent Transportation System (ITS). The main challenge that vehicle ReID presents is the large intra-class and small inter-class variability of vehicles appearance, followed by illumination changes, different viewpoints and scales, lack of labelled data and camera resolution. To address these problems, we present a vehicle ReID system that combines different ReID models, including appearance and orientation deep learning features. Additionally, for results refinement re-ranking and a post-processing step taking into account the vehicle trajectory information provided by the CityFlow-ReID dataset are applied.
Related Material
[pdf][bibtex]@InProceedings{Moral_2020_CVPR_Workshops,
author = {Moral, Paula and Garcia-Martin, Alvaro and Martinez, Jose M.},
title = {Vehicle Re-Identification in Multi-Camera Scenarios Based on Ensembling Deep Learning Features},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}
}
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