文章摘要
朱宜强,丁锡龙.应用于运动目标检测的改进参数估计背景建模研究[J].机床与液压,2018,46(12):171-176.
.Background modeling based on improved parameter estimation for moving target detection[J].Machine Tool & Hydraulics,2018,46(12):171-176
应用于运动目标检测的改进参数估计背景建模研究
Background modeling based on improved parameter estimation for moving target detection
  
DOI:10.3969/j.issn.1001-3881.2018.12.029
中文关键词: 运动目标检测  Student t  背景建模  背景分割
英文关键词: Moving object detection, Student-t, Background modeling, Background segmentation
基金项目:
作者单位E-mail
朱宜强 芜湖职业技术学院 电气工程学院 zhuyiqiang111@126.com 
丁锡龙 芜湖职业技术学院 电气工程学院  
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中文摘要:
      为了提高目标检测的识别率,提出了一种基于改进Student-t分布参数估计的背景建模算法,适用于各种运动视频的目标检测,如体育视频应用领域。首先对传统基于有限混合模型的背景建模方法进行了分析,然后利用Student-t分布构建了背景模型参数估计方法,该方法通过改进期望值最大算法完成参数估计,并对参数空间进行了分割。仿真试验结果显示:相比高斯多态背景建模,K-均值和模糊 C-均值聚类背景建模分割效果较好,因此运动目标检测精确度更高,且训练速度更快。验证了提出算法的有效性和先进性。
英文摘要:
      In order to improve the recognition rate of target detection, a background modeling algorithm based on improved Student-t distribution parameter estimation is proposed in this paper, which is suitable for all kinds of motion video target detection such as sports video application field. Firstly, the background modeling method based on the traditional finite model is analyzed. Then, the Student-t distribution is used to construct the background model parameter estimation method. The method is used to complete the parameter estimation by improving the maximum expectation algorithm, and the parameter space is segmented. The simulation results show that the segmentation effect of the proposed algorithm is better as compared with the Gaussian background modeling, K-means and fuzzy C-means clustering background modeling, and the motion target detection accuracy is higher and the training speed is faster. The results verify that the validity and advancement of the proposed algorithm.
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