Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel visual-based approach that can detect brake lights at night by analyzing the tail lights based on the thee-dimensional Nakagami imaging which can provide robust information of brake lights. Instead of using the knowledge of the heuristic features, such as symmetry and position of rear facing vehicle, size and so forth, we focus on extracting the invariant features based on modeling the scattering of brake lights and therefore can conduct the detection process in a part-based manner. Experiment from extensive dataset shows that our proposed system can effectively detect vehicle braking under different lighting and traffic conditions, and thus prove its feasibility in real-world environments. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-27355-1_81 | MMM |
Keywords | Field | DocType |
extensive dataset,real-world environment,event detection,detection process,different lighting,part-based manner,visual-based spatiotemporal analysis,brake light,robust information,nighttime vehicle,novel visual-based approach,proposed system,heuristic feature | Computer vision,Brake,Heuristic,Computer science,Nakagami distribution,Artificial intelligence,Invariant (mathematics),Spatiotemporal Analysis,Traffic conditions | Conference |
Volume | ISSN | Citations |
7131 | 0302-9743 | 2 |
PageRank | References | Authors |
0.42 | 10 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Duan-Yu Chen | 1 | 296 | 28.79 |
| Chia-Hsun Chen | 2 | 5 | 1.57 |