Please use this identifier to cite or link to this item:
Title: Unsupervised vehicle re-identification using triplet networks
Authors: Marin-Reyes, Pedro Antonio
Bergamini, Luca
Lorenzo-Navarro, Javier 
Palazzi, Andrea
Calderara, Simone 
Cucchiara, Rita
UNESCO Clasification: 120304 Inteligencia artificial
Issue Date: 2018
Publisher: 2160-7508
Journal: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Conference: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 
31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018 
Abstract: Vehicle re-identification plays a major role in modern smart surveillance systems. Specifically, the task requires the capability to predict the identity of a given vehicle, given a dataset of known associations, collected from different views and surveillance cameras. Generally, it can be cast as a ranking problem: given a probe image of a vehicle, the model needs to rank all database images based on their similarities w.r.t the probe image. In line with recent research, we devise a metric learning model that employs a supervision based on local constraints. In particular, we leverage pairwise and triplet constraints for training a network capable of assigning a high degree of similarity to samples sharing the same identity, while keeping different identities distant in feature space. Eventually, we show how vehicle tracking can be exploited to automatically generate a weakly labelled dataset that can be used to train the deep network for the task of vehicle re-identification. Learning and evaluation is carried out on the NVIDIA AI city challenge videos.
ISBN: 9781538661000
ISSN: 2160-7508
DOI: 10.1109/CVPRW.2018.00030
Source: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops[ISSN 2160-7508],v. 2018-June (8575466), p. 166-171
Appears in Collections:Actas de congresos
Show full item record


checked on Sep 25, 2022


checked on Sep 25, 2022

Page view(s)

checked on Jun 11, 2022

Google ScholarTM




Export metadata

Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.