A computer vision algorithm for locating and recognizing traffic signal control light status and countdown time. Issue 5 (3rd September 2021)

• Record Type: Journal Article
• Authors: Chen, Xiaofeng; Chen, Yu; Zhang, Guohui
• Is Part Of: Journal of intelligent transportation systems. Volume 25:Issue 5(2021)
• Page Start: 533
• Page End: 546
• Publication Date: 2021-09-03
• Subjects: Cascade filtering -- color recognition -- digital countdown time recognition -- multi-color space fusion; Intelligent transportation systems -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.312
• DOI: 10.1080/15472450.2021.1871611
• Languages: English
• ISSNs: 1547-2450

Abstract

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.