Name
Title | ||
|---|---|---|
A Cost-Effective Framework for Automated Vehicle-Pedestrian Near-Miss Detection Through Onboard Monocular Vision. |
Abstract | ||
|---|---|---|
Onboard monocular cameras have been widely deployed in both public transit and personal vehicles. Obtaining vehicle-pedestrian near-miss event data from onboard monocular vision systems may be cost-effective compared with onboard multiple-sensor systems or traffic surveillance videos. But extracting near-misses from onboard monocular vision is challenging and little work has been published. This paper fills the gap by developing a framework to automatically detect vehicle-pedestrian near-misses through onboard monocular vision. The proposed framework can estimate depth and real-world motion information through monocular vision with a moving video background. The experimental results based on processing over 30-hours video data demonstrate the ability of the system to capture near-misses by comparison with the events logged by the Rosco/MobilEye Shield+ system which includes four cameras working cooperatively. The detection overlap rate reaches over 90% with the thresholds properly set. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CVPRW.2017.124 | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
Field | DocType | Volume |
Monocular vision,Computer vision,Pedestrian,Computer science,Feature extraction,Event data,Artificial intelligence,Monocular,Near miss | Conference | 2017 |
Issue | ISSN | Citations |
1 | 2160-7508 | 1 |
PageRank | References | Authors |
0.36 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ke Ruimin | 1 | 89 | 6.69 |
| Jerome Lutin | 2 | 1 | 0.36 |
| Jerry Spears | 3 | 1 | 0.36 |
| Yinhai Wang | 4 | 292 | 39.37 |