Nighttime Vehicle Lamp Detection and Tracking With Adaptive Mask Training
In recent years, traffic flow controlling and monitoring have become important issues in intelligent transportation systems (ITSs). For example, vehicle flow management can positively benefit the ITSs from accurate road vehicle detection and counting surveillance systems. In this paper, a high-accuracy vehicle detection mechanism with robust and adaptive vehicle mask training is proposed to effectively detect vehicles in nighttime scenarios. First, a preprocessing step is applied to segment vehicle lamps and filter out reflections. Next, the similarity and symmetric degree of light components are classified into vehicle lamp candidates. A simple fog lamp elimination method is proposed for adapting the proposed method to various road conditions. Then, the mask is trained by a combination of color distribution and weighting information, and it is utilized in the first stage of vehicle detection. The experimental results verify that the proposed method is effective and adaptive for the detection of vehicles at night.