Name
Abstract | ||
|---|---|---|
Vehicle braking event in front is a critical issue for drivers in general road environments. The cost down of general cameras makes it feasible to having an intelligent system of visual-based detector in front for forward collision avoidance and mitigation. When driving at nighttime, vehicles in front are generally visible by their tail and brake lights. The brake lights are particularly important due to their consequent events that drivers need to focus on. Therefore, 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 Nakagami-m distribution. 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 finding the invariant features modeling the brake lights scattering by Nakagami imaging 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 |
|---|---|---|
2011 | 10.1109/ITSC.2011.6082937 | ITSC |
Keywords | Field | DocType |
object detection,traffic engineering computing,vehicles,nakagami imaging,nakagami-m distribution,forward collision avoidance,general road environments,intelligent system,part-based manner,rear facing vehicle,scattering-based brake-light modeling,tail lights,vehicle braking event,visual-based detector,brake light detection,braking | Object detection,Brake,Heuristic,Simulation,Collision,Nakagami distribution,Invariant (mathematics),Scattering,Engineering,Detector | Conference |
ISSN | ISBN | Citations |
2153-0009 | 978-1-4577-2198-4 | 1 |
PageRank | References | Authors |
0.39 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Duan-Yu Chen | 1 | 296 | 28.79 |
| Yu-Hao Lin | 2 | 20 | 2.46 |
| Hui-Ping Kuo | 3 | 1 | 0.39 |