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.
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 -