Enhancing vehicle re-identification via synthetic training datasets and re-ranking based on video-clips information
EntityUAM. Departamento de Tecnología Electrónica y de las Comunicaciones
10.1007/s11042-023-14511-0Multimedia Tools and Applications (2023): 1-21
ISSN1380-7501 (print); 1573-7721 (online)
Funded byOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature; This work is part of the preliminary tasks related to the Harvesting Visual Data (HVD) project (PID2021-125051OB-I00) funded by the Ministerio de Ciencia e Innovacion of the Spanish ´ Government
ProjectGobierno de España. PID2021-125051OB-I00
SubjectsDeep learning; Image processing; Surveillance videos; Vehicle re-identification; Telecomunicaciones
Rights© The Author(s) 2023
Esta obra está bajo una Licencia Creative Commons Atribución 4.0 Internacional.
Vehicle re-identification (ReID) aims to find a specific vehicle identity across multiple non-overlapping cameras. The main challenge of this task is the large intra-class and small inter-class variability of vehicles appearance, sometimes related with large viewpoint variations, illumination changes or different camera resolutions. To tackle these problems, we proposed a vehicle ReID system based on ensembling deep learning features and adding different post-processing techniques. In this paper, we improve that proposal by: incorporating large-scale synthetic datasets in the training step; performing an exhaustive ablation study showing and analyzing the influence of synthetic content in ReID datasets, in particular CityFlow-ReID and VeRi-776; and extending post-processing by including different approaches to the use of gallery video-clips of the target vehicles in the re-ranking step. Additionally, we present an evaluation framework in order to evaluate CityFlow-ReID: as this dataset has not public ground truth annotations, AI City Challenge provided an on-line evaluation service which is no more available; our evaluation framework allows researchers to keep on evaluating the performance of their systems in the CityFlow-ReID dataset
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Google Scholar:Moral De Eusebio, Paula - García Martín, Álvaro - Martínez, José M. - Bescos Cano, Jesús
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