Abstract
In this paper, we propose a real-time system using vehicle back-up camera to alert for potential back-up collisions. We developed a highly efficient algorithm, combining segmenting pedestrians and vehicles from moving background using local optical flow value, and a scale adaptive method using Deformable Part Model to detect objects at different distances. To test out algorithm, we created our own vehicle back-up dataset that contains rich scenes recorded from a back-up camera on moving/stationary vehicles with unique and challenging scenarios such as frequent occlusion with cluttered and moving background, and we made this dataset available to public for other researchers. Experiments on the dataset shows that our algorithm achieves high accuracy in near real-time, and it is about 10 times faster than the comparable state-of-the-art algorithm.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - International Conference on Image Processing, ICIP |
| Publisher | IEEE Computer Society |
| Pages | 2275-2279 |
| Number of pages | 5 |
| Volume | 2015-December |
| ISBN (Print) | 9781479983391 |
| DOIs | |
| State | Published - Dec 9 2015 |
| Event | IEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada Duration: Sep 27 2015 → Sep 30 2015 |
Other
| Other | IEEE International Conference on Image Processing, ICIP 2015 |
|---|---|
| Country | Canada |
| City | Quebec City |
| Period | 9/27/15 → 9/30/15 |
Fingerprint
Keywords
- Computer Vision
- Deformable Part Model
- Latent SVM
- Optical Flow
- Vehicle Safety
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
- Signal Processing
Cite this
Real-time vehicle back-up warning system with a single camera. / Cao, Jun; Wang, Yilin; Li, Baoxin.
Proceedings - International Conference on Image Processing, ICIP. Vol. 2015-December IEEE Computer Society, 2015. p. 2275-2279 7351207.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Real-time vehicle back-up warning system with a single camera
AU - Cao, Jun
AU - Wang, Yilin
AU - Li, Baoxin
PY - 2015/12/9
Y1 - 2015/12/9
N2 - In this paper, we propose a real-time system using vehicle back-up camera to alert for potential back-up collisions. We developed a highly efficient algorithm, combining segmenting pedestrians and vehicles from moving background using local optical flow value, and a scale adaptive method using Deformable Part Model to detect objects at different distances. To test out algorithm, we created our own vehicle back-up dataset that contains rich scenes recorded from a back-up camera on moving/stationary vehicles with unique and challenging scenarios such as frequent occlusion with cluttered and moving background, and we made this dataset available to public for other researchers. Experiments on the dataset shows that our algorithm achieves high accuracy in near real-time, and it is about 10 times faster than the comparable state-of-the-art algorithm.
AB - In this paper, we propose a real-time system using vehicle back-up camera to alert for potential back-up collisions. We developed a highly efficient algorithm, combining segmenting pedestrians and vehicles from moving background using local optical flow value, and a scale adaptive method using Deformable Part Model to detect objects at different distances. To test out algorithm, we created our own vehicle back-up dataset that contains rich scenes recorded from a back-up camera on moving/stationary vehicles with unique and challenging scenarios such as frequent occlusion with cluttered and moving background, and we made this dataset available to public for other researchers. Experiments on the dataset shows that our algorithm achieves high accuracy in near real-time, and it is about 10 times faster than the comparable state-of-the-art algorithm.
KW - Computer Vision
KW - Deformable Part Model
KW - Latent SVM
KW - Optical Flow
KW - Vehicle Safety
UR - http://www.scopus.com/inward/record.url?scp=84956676882&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84956676882&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2015.7351207
DO - 10.1109/ICIP.2015.7351207
M3 - Conference contribution
SN - 9781479983391
VL - 2015-December
SP - 2275
EP - 2279
BT - Proceedings - International Conference on Image Processing, ICIP
PB - IEEE Computer Society
ER -