摘要
运动检测是基于视频的智能交通系统的基础,为了能够快速、准确的适应背景变化,本文提出了一种基于局域灰度分布的运动检测算法,利用图像块的灰度分布特征,构造相应的离散概率模型,并采用自适应的模型参数更新方法。实验证明,算法能够自适应交通场景中背景的多模态性、动态背景、光照变化以及一定程度的遮挡等情况,并且训练时间短,每帧的处理时间也能够满足实时性要求。
Motion detection is an essential step in the intelligent traffic system based on video. In order to correctly deal with variances of background quickly, this paper proposes a novel adaptive motion detection algorithm based on local grey-level distribution. It constructs the discrete probabilistic model for each image block and adaptively updates the, parameters of the model. Experiments show that this method can effectively handle the practical difficulties in traffic scenes,including muhimodal background, dynamic background, illumination changes, and partial occlusion. In addition, this algorithm requires less training samples and can satisfy real time requirement of processing time per frame.
出处
《信号处理》
CSCD
北大核心
2008年第2期209-212,共4页
Journal of Signal Processing
基金
中科院自动化所-中国科学技术大学智能科学与技术联合实验室自主研究课题基金(No.A0602)支持
关键词
智能交通系统
运动检测
灰度分布
Intelligent Transportation System
Motion detection
Grey-level Distribution
