Abstract

LEDs are widely employed as traffic lights. Because most LED traffic lights are driven by alternative power, they blink at high frequencies, even at twice their frequencies. We propose a method to detect a traffic light from images captured by a high-speed camera that can recognize a blinking traffic light. This technique is robust under various illuminations because it can detect traffic lights by extracting information from the blinking pixels at a specific frequency. The method is composed of six modules, which includes a band-pass filter and a Kalman filter. All the modules run simultaneously to achieve real-time processing and can run at 500 fps for images with a resolution of 800 × 600. This technique was verified on an original dataset captured by a high-speed camera under different illumination conditions such as a sunset or night scene. The recall and accuracy justify the generalization of the proposed detection system. In particular, it can detect traffic lights with a different appearance without tuning parameters and without datasets having to be learned.

Highlights

  • Automobiles play an important role in modern society

  • This study considered a case where the traffic light was driven by a 50-Hz alternate current (AC); the lamp blinks at 100 fps

  • The video sequences were taken in an urban street in East Japan using a Basler high-speed camera

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Summary

Introduction

Automobiles play an important role in modern society. Modern cars are cheaper, faster, and conbenient to use in many cases; many accidents occur every year. An automatic driving system can make critical safety decisions in accordance with the state of the traffic lights. It should be able to reliably recognize the state of a traffic light from a long distance and in real time. Such an automatic detection system has not been developed yet. As discussed in [3], traffic light detection for complex scenes is a significant challenge. Some of the factors contributing to these complex scenes include various illumination conditions; incomplete shapes due to occlusion; very few pixels for detecting distant traffic lights; and motion blurring due to high-speed driving

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