基于视频的交通监控技术研究

Research of Vision-Based Traffic Surveillance Technology

提出了一种基于视频的实时交通监控系统。系统包括背景建模和更新,运动目标分割,特征提取,以及车辆的跟踪、分类和统计。以改进的高斯混合模型算法估算并实时更新背景图像,以背景差分法检测运动车辆并作后处理,以卡尔曼滤波算法进行车辆跟踪。通过分析车辆轮廓及其外接盒获得车辆的明显特征,最后使用提取到的特征和虚拟检测线方法对车辆进行分类和统计。实验结果表明所提出的方法用于交通监控是可行的。

A vision-based realtime traffic surveillance system is proposed. The system includes background modeling and updating, moving object segmentation, feature extraction, and vehicle tracking, classification and counting. The background image is estimated and updated in realtime by improved Gaussian Mixture Model. Vehicles are detected by background subtraction followed by post-processing steps,and tracked by Kalman filtering algorithm. By analyzing the contours of vehicles and their corresponding bounding box, salient discriminative features of vehicles are obtained.The vehicle classification and counting can be achieved by extracted features and virtual detecting line.Experimental results show the proposed method is feasible for traffic surveillance.