摘要
针对夜间高速光照条件差、车灯种类多样、环境因素干扰等导致的车辆难以检测的问题,提出了一种基于视频的夜间车辆的检测与跟踪算法。该方法首先将OTSU与一维最大熵阈值分割算法相结合来实现车灯的提取,剔除非车灯光源;然后利用车灯的时空特性完成车灯的匹配,解决了一车多灯和并排同速车辆的问题;最后利用kalman滤波器完成车灯的预测跟踪。在交通弱光流畅交通、正常光流畅交通和正常光拥堵交通3种应用场景下对所提算法进行应用和结果分析,实验结果表明所提方法在保证实时性的同时具有较高的准确率。
In the night , the vehicles on highway are difficult to detect because of a variety of factors,such as bad high-way l ighting conditions and dif ferent type of lights. To solve the problem, a video-based night time vehicle detection and tracking algorithm was proposed. Firstly, this algorithm combines OTSU and one dimensional maximum entropy threshol-ding algorithm to extract vehicle lights, el iminating non-vehicle lights. After that, this method makes use of temporal and spatial characteristics of light to distinguish a car with mul tiple l ights and side-by-side vehicles. Final ly, the kalman filter is used to predict and track the vehicle lights. This paper analyzed the result of the algorithm in three dif ferent ap-plication scenarios, weak light smooth traffic, normal light smooth traffic and normal light congestion traffic. The experi-mental results prove that the proposed method has high accuracy and pretty real-time performance.
出处
《计算机科学》
CSCD
北大核心
2017年第B11期233-237,共5页
Computer Science
基金
国家自然科学基金项目(61672464)资助
关键词
夜间高速
车辆检测
智能交通
Night t ime highway, Vehicle detection, Smart traffic
