摘要
为实现对闯红灯车辆的准确快速检测,提出了基于混合高斯模型和GPU的车辆闯红灯检测算法。先用时间均值法根据监控系统运行后的第1个红灯期间采集的视频数据构建背景图像,再利用这个背景图像初始化混合高斯模型参数,进而采用混合高斯模型检测运动车辆;为实现实时检测,采用GPU并行计算对算法进行了实现。实验结果表明,该算法克服了实际道路监控视频中出现的混合高斯模型初始化参数选择不合理的问题,加快了混合高斯模型的收敛速度,实时性好。
In order to accurately and rapidly detect vehicles red-light running,this paper presents vehicles red-light running detection algorithm based on gaussian mixture model and GPU.First background image is constructed by mean-time algorithm from the video data during the first red light,and then the background image is used to initialize Gaussian mixture model parameters,and the gaussian mixture model is employed to test vehicle movement.In order to realize the real-time detection,the authors design and implement vehicles red-light running detection algorithm with GPU parallel computing.Experimental results show that it overcomes the problem that initialization parameters in Gaussian mixture model are unreasonably selected in real surveillance video and speeds up the convergence rate of Gaussian mixture model and the improved algorithm proposed in this paper is effective and real time.
出处
《西华大学学报（自然科学版）》
CAS
2012年第2期9-13,共5页
Journal of Xihua University:Natural Science Edition
基金
四川省网络智能信息处理高校重点实验室(SGXZD1002-10)
西华大学研究生创新基金项目(Ycjj201119)
关键词
车辆闯红灯检测
混合高斯模型
GPU并行计算
时间均值法
detection for vehicles red-light running
gaussian mixture model
GPU parallel computing
mean-time algorithm
