基于机器视觉的交通拥堵评估系统

摘要

传统的交通灯采用固定配时模式,缺乏灵活性、智能性.针对上述问题,提出一种基于机器视觉的交通拥堵评估系统对采集到的视频进行智能分析处理.首先,利用提取的梯度直方图特征和AdaBoost级联分类器实现对车辆的检测,并辅以RFID来实现车辆计数;进而通过Spark大数据分析平台而评估出当前的交通拥堵情况.实验表明本系统能根据当前的实际交通情况智能调整交通灯的变换时间,达到动态缓解交通压力的目的.%The traditional traffic light systems are poor in flexibility and intelligencefor their fixed timing modes. In view of the above problems, a machine-vision-based traffic congestion evaluation system is presented to evaluate the current situation of traffic jams in this paper based on the collected video intelligent analysis and processing. Vehicle counting is firstly realized by HOG-feature analysis, AdaBoost method and RFID technology. Traffic states are evaluated in the Spark platform. The result of the experiments shows that our system can realize adjusted transformation time of traffic lights according to actual situation of the current traffic environment, then achieve the purpose of relieving traffic pressure dynamically.