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浙江大学学报(工学版)
J4  2010, Vol. 44 Issue (3): 458-462    DOI: 10.3785/j.issn.1008973X.2010.03.008
    
Improved algorithm of subtractive clustering for object location  in video sequences
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Abstract  

 For the weakness appeared when using subtractive clustering for moving object location in video sequences, six optimization techniques of subtractive clustering were proposed. Different effective radius in different dimensions was considered and corresponding extended method was proposed. Techniques of downsampling, choosing density function and redefining clustering data set based on certain grid method were put forward. A much more accurate and further segmentation method based on fuzzy membership was also presented. Comparison with the conventional method showed the superiority of the proposed subtractive clustering method to different data sets.

Published: 20 March 2012
CLC:  TP391.41  
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Cite this article:

SUN Zhi-Hai, KONG Mo-Ceng, SHU Shan-An. Improved algorithm of subtractive clustering for object location  in video sequences. J4, 2010, 44(3): 458-462.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008973X.2010.03.008     OR     http://www.zjujournals.com/eng/Y2010/V44/I3/458


视频目标定位的减法聚类改进算法

针对减法聚类算法用于视频运动目标定位时存在的不足,提出了6点优化技术,即采用不同维度的邻域半径改进了原算法采用固定邻域半径的不足；修正目标邻域半径的取值,改进了原算法无法准确描述不同尺度目标定位效果的问题；引入下采样技术、改变密度值函数及构造网格重定义数据集3种方法以提高算法的定位效率；引入模糊隶属度对视频运动目标做进一步精确定位,解决了原算法无法精确定位的问题.实验结果表明,改进后的定位算法可获得更好的定位效果.

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