Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/55091
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.
URI: http://hdl.handle.net/10553/55091
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
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