A computer vision algorithm for locating and recognizing traffic signal control light status and countdown time

Abstract

It is of practical importance for individual vehicles to automatically identify traffic signal light status to facilitate their decision makings for traffic safety performance enhancement and operation efficiency maximization when they are approaching intersections, especially in near-future fully-or semi-autonomous vehicle-penetrated traffic systems. Traditional methods aim to directly extract information from the light emitting area of a traffic signal light head, but this can easily result in missed or false signal light status identification due to changes in detection distance and lighting conditions. To address these research gaps, an innovative computer vision-based algorithm is developed for signal control light status identification and countdown time recognition. Based on the unique characteristics of black traffic light boards, color segmentation is conducted in both RGB and HSV channels, and traffic light boards and countdown time display boards are located through cascade filtering algorithms. In the HSV color space, the H component can be extracted and highlighted to determine the current signal status by distinguishing the distribution of the pixel points of the H component. Based on the nearest neighbor interpolation algorithm, an adaptive threading method is developed to improve countdown time recognition. The experimental tests were conducted to verify the effectiveness of the developed approach under various scenarios with acceptable recognition accuracy and execution efficiency. The developed approach can be applied and integrated with other detection and control algorithms to strengthen vehicle-crossing safety performance and operational efficiency at intersections.

Keywords:

• Cascade filtering
• color recognition
• digital countdown time recognition
• multi-color space fusion

Acknowledgments

The authors would like to thank the Editor and the three anonymous reviewers for their constructive comments and valuable suggestions to improve the quality of the article. The authors are also grateful for the financial support from Department of Science and Technology of Shaanxi Province, China (Project No. 2019GY-117).

Disclosure statement

No potential conflict of interest was reported by the author(s).