摘要
针对道路交通中行人的特点,从参数更新、背景估计和前景分割三个方面改进传统的混合高斯模型,提出一种有效的行人检测方法。首先,利用基于图像分割的参数更新模型,减少将静止前景判定为背景的可能性;其次,采用前景融合时间调整机制,控制前景融入背景的时间;最后,引入均值权值的概念,优化前景分割的条件。试验结果表明,改进的算法优于传统的混合高斯模型,具有良好的鲁棒性和自适应性,可正确检测出移动速度缓慢或静止的行人。
Aiming at the peculiarity of pedestrian in the road traffic, an effective pedestrian detection method was proposed based on an improved Gaussian mixture model（GMM） in 3 aspects： parameter updating, background estimation and foreground segmentation. The possibility of misjudging the static foreground as the background was reduced using a parameter updating model based on the image segmentation. The time of the foreground merging into the background was controlled applying the adjustment scheme of foreground merging time. The foreground segmentation condition was optimized by introducing the concept of average weight. The test results showed that the improved algorithm is better than the traditional GMM. It is characterized by good robustness and adaptability, able to detect the slow moving even static pedestrian.
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2011年第1期41-45,共5页
Journal of Jilin University:Engineering and Technology Edition
基金
'973'国家重点基础研究发展计划项目(2006CB705500)
国家自然科学基金项目(50778015)
中国人民大学科学研究基金项目(07XND012)
关键词
交通运输系统工程
智能交通系统
行人检测
背景提取
混合高斯模型
engineering of communications and transportation system
intelligent transportation system
pedestrian detection
background extraction
Gaussian mixture model（GMM）
