ABSTRACT

This paper addresses the problem of traffic variable estimation and traffic state classification of highway traffic, from video. To solve this problem, we propose to use the Interactive Multiple Model (IMM) filter with a multi-class macroscopic model. This filter runs two Extended Kalman Filters (EKF) to smooth the measured traffic parameters. In addition, the models’ probabilities that it provides are exploited to simply classify the traffic state as either free or congested, without the need for a training phase. The evaluation of the proposed system using simulated traffic parameters shows that it achieves a very accurate traffic state classification. The system was also tested in the real world, using video data acquired on a freeway by camera sensors. The obtained classification rates are comparable to those obtained by SVM classification, but at a significantly lower computational load.

摘要

本文从视频中解决了高速公路交通的交通变量估计和交通状态分类问题。为了解决这个问题，我们建议使用具有多类宏观模型的交互式多模型（IMM）滤波器。该滤波器运行两个扩展卡尔曼滤波器 (EKF) 以平滑测量的流量参数。此外，它提供的模型概率被用来简单地将交通状态分类为空闲或拥塞，而不需要训练阶段。使用模拟交通参数对所提出系统的评估表明，它实现了非常准确的交通状态分类。该系统还在现实世界中进行了测试，使用的是在高速公路上通过摄像头传感器获取的视频数据。获得的分类率与通过 SVM 分类获得的分类率相当，