摘要
针对智能交通车流量检测系统,提出了一种适用于嵌入式系统的快速轻量背景建模方法。该方法先由帧差法过滤视频序列,抽取运动物体少的帧进行存储,再利用改进的高斯模型快速学习获得基础模型,并结合帧差和像素统计的方法对背景模型进行自适应更新,对传统混合高斯模型的缺陷进行了改善,在基于TMS320DM648的图像处理客户端上表现出较好的实时性和天气适应性,以及更高的处理效能。
Aiming at intelligent traffic flow detection system, a fast and light weighted background modeling algorithm based on embedded system is proposed. It filters video sequences by frame difference method, buffers the extracted motion-less frames, and adopts an improved Gaussian model to learn the buffered frames in result of a basic model. To improve the defects of Gaussian mixture model, an adaptive updating manner combining frame difference and pixel statistic have been realized, which shows a better real-time image processing performance and ideal weather adaptability on a TMS320DM648 platform.
出处
《太赫兹科学与电子信息学报》
2013年第2期286-290,共5页
Journal of Terahertz Science and Electronic Information Technology
基金
国家自然科学基金委员会和中国工程物理研究院联合基金资助项目(11176018)
关键词
背景建模
帧差法
高斯模型
运动目标
自适应
background modeling
frame difference
Gaussian model
motion object
adaptive
