Authors:
Shodai Horima
and
Kazunori Onoguchi
Affiliation:
Hirosaki University, Japan
Keyword(s):
Traffic light detection, Traffic light recognition, Color identification, Circular Separability Filter, ITS.
Related
Ontology
Subjects/Areas/Topics:
Classification
;
Feature Selection and Extraction
;
Model Selection
;
Pattern Recognition
;
Theory and Methods
Abstract:
This paper proposes the camera-based approach to recognize the traffic light for driver assistance. The circular separability filter applied to RGB images extracts the area of the traffic light. The separability has large value in the boundary where the intensity between two areas changes like the step and it doesn't depend on the intensity difference (height of the step). Scanning the circular mask in each RGB image, the separability is calculated. The separability becomes large in an area where a color is homogeneous and a shape is similar to the circle. Therefore, the pixel with large separability is selected as the candidate of the traffic light. Unlike the conventional method which calculates the circularity from the binarized region, the proposed method can identify the traffic light whose outline is indistinct and whose radius is small. At first, the proposed method removes the region where the saturation is low and the brightness is extremely low or high because there is f
ew possibility that the traffic light is included in these regions. Next, the circular mask is scanned in each RGB image captured from the on-vehicle color camera and the separability between the inside circle and the outside ring is calculated. The maximum value of separability calculated in RGB images is selected as the separability of each pixel. Pixels with large separability are detected as the candidate region of the traffic light. Finally, the candidate region around which inactive traffic lamps exist is identified as the traffic light. Experiments recognizing various traffic lights under various weathers and time show the effectiveness of the proposed method.
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