Name
Abstract | ||
|---|---|---|
Detection of arrow traffic light is a focal point research in autonomous vehicle, and in urban environment it is the basic technique. However, most researches mainly concern the circular traffic lights. A novel algorithm is proposed in this paper to resolve the problems of detection and recognition of arrow traffic lights. Two sub-modules, detection module and recognition module, are introduced in the main framework. In detection submodule, the color space conversion, binarization and morphology features filtering methods are performed to get the regions of candidates of blackboards. For getting the regions of arrow of traffic lights, segmentation based on the YCbCr color space is used in the cropping image, which is cropped from original image by the region of blackboard. In recognition sub-module, Gabor wavelet transform and 2D independent component analysis(2DICA) are used to extract traffic light candidate's features for features of the arrow traffic lights. A library for recognition has been built, and experimental results show that rate of recognition exceeds 91%. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CISDA.2012.6291516 | CISDA |
Keywords | Field | DocType |
detection module,recognition module,wavelet transforms,traffic engineering computing,color space conversion method,image segmentation,segmentation,ycbcr color space,image cropping,automobiles,mobile robots,arrow traffic light,morphology features filtering method,image recognition,independent component analysis,2d independent component analysis,arrow traffic light detection,real-time recognition system,intelligent vehicle,arrow traffic light recognition,traffic light candidate feature extraction,feature extraction,autonomous vehicle,gabor wavelet transform,urban environment,binarization method,gabor wavelet,filtering theory,2dica,road traffic,circular traffic light,robot vision,image colour analysis,strontium | Computer vision,Color space,Segmentation,Computer science,Gabor wavelet,Filter (signal processing),Feature extraction,Image segmentation,Independent component analysis,Artificial intelligence,Wavelet transform | Conference |
ISBN | Citations | PageRank |
978-1-4673-1416-9 | 11 | 0.80 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zixing Cai | 1 | 1525 | 66.96 |
| Yi Li | 2 | 11 | 0.80 |
| Mingqin Gu | 3 | 11 | 0.80 |