Abstract
An adaptive background-learning and subtraction method is proposed and applied to two real-life traffic video sequences to obtain more accurate spatiotemporal information on the vehicle objects. When paired with the image segmentation, the method is robust under many conditions. A key advantage of the method is that it is fully automated and unsupervised; it performs the generation of background images using a self-triggered mechanism.
Original language | English (US) |
---|---|
Pages (from-to) | 50-58 |
Number of pages | 9 |
Journal | IEEE Robotics and Automation Magazine |
Volume | 12 |
Issue number | 1 |
DOIs | |
State | Published - Mar 2005 |
Keywords
- Intelligent transportation systems
- Robotic vision
- Segmentation
- Vehicle tracking
- Video analysis
ASJC Scopus subject areas
- Control and Systems Engineering