Proceedings of the 2016 International Conference on Computer Science and Electronic Technology

A Large-scale and Global Car Dataset for Verification

Authors
Lingji Hu, Xingcheng Luo, Jianhua Deng, Fengjie Lai, Jian Hu, Yongbin Yu
Corresponding Author
Lingji Hu
Available Online August 2016.
DOI
https://doi.org/10.2991/cset-16.2016.12How to use a DOI?
Keywords
Car model, dataset, Gcars, car verification
Abstract
Few researches focus on the larger-scale car model dataset compared with other objects in computer vision for verification, such as classification and face verification. In this paper, we present our on-going effort in collecting a large-scale and global dataset, Gcars, for improving related car model research. This dataset contains not only the most famous global car models but also the most locals in China, where all car images are collected from public website and the car hierarchy is a three-layer, make, model and type. We also demonstrate the most important application, car verification, exploiting the dataset. The performance in terms of verification accuracy is better than that of the benchmark dataset, namely CompCars, which is the similar and famous dataset, by using the deep learning framework Caffe.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
2016 International Conference on Computer Science and Electronic Technology
Part of series
Advances in Computer Science Research
Publication Date
August 2016
ISBN
978-94-6252-213-8
ISSN
2352-538X
DOI
https://doi.org/10.2991/cset-16.2016.12How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Lingji Hu
AU  - Xingcheng Luo
AU  - Jianhua Deng
AU  - Fengjie Lai
AU  - Jian Hu
AU  - Yongbin Yu
PY  - 2016/08
DA  - 2016/08
TI  - A Large-scale and Global Car Dataset for Verification
BT  - 2016 International Conference on Computer Science and Electronic Technology
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/cset-16.2016.12
DO  - https://doi.org/10.2991/cset-16.2016.12
ID  - Hu2016/08
ER  -