Conference paper
UNMC-VIER AutoVision database
2010 International Conference on Computer Applications and Industrial Electronics, ICCAIE 2010, pp.650-654
International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010), 2010 (Kuala Lumpur, Malaysia, 05-Dec-2010–07-Dec-2010)
Institute of Electrical and Electronics Engineers
2010
Abstract
Designing a driver assistance system has become a trend in the automotive technology to improve the security and efficiency of driving. However, there is no standard on-road database to verify the performance and effectiveness of algorithms. In this paper, an automotive vision database is created to assist researchers analyzing their designed algorithm in a more convincing way. The UNMC-VIER AutoVision database composes of a series of single view videos embodying the information of traffic signs, vehicles and single/multiple lanes. In addition, multi-views videos, with the aid of three cameras are included in the database providing more visual information in the panoramic view of traffic scene for analysis. The standard setup and calibration of the database is discussed in the paper. Some applications are discussed along with the use of the database.
Details
- Title
- UNMC-VIER AutoVision database
- Authors
- K H Lim (Author) - University of Nottingham Malaysia CampusA C Le Ngo (Author) - University of Nottingham Malaysia CampusK P Seng (Author) - University of Nottingham Malaysia CampusLi-Minn Ang (Author) - University of Nottingham Malaysia Campus
- Publication details
- 2010 International Conference on Computer Applications and Industrial Electronics, ICCAIE 2010, pp.650-654
- Conference details
- International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010), 2010 (Kuala Lumpur, Malaysia, 05-Dec-2010–07-Dec-2010)
- Publisher
- Institute of Electrical and Electronics Engineers
- Date published
- 2010
- DOI
- 10.1109/ICCAIE.2010.5735015
- ISBN
- 9781424490547
- Organisation Unit
- University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Engage Research Lab
- Language
- English
- Record Identifier
- 99513800602621
- Output Type
- Conference paper
Metrics
75 Record Views