期刊文献+

尺度方向自适应的减法聚类视频运动目标定位 被引量:1

Scale and Direction Adaptive Locating of Video Moving Objects with Subtractive Clustering
下载PDF
导出
摘要 针对减法聚类算法对视频运动目标进行定位时无法获取目标尺度及方向参数的问题,本文提出了一种可获取待定位目标尺度及方向参数的视频运动目标定位算法。该算法在减法聚类算法预定位目标位置及获得目标个数的基础上,进一步采用模糊C均值聚类对目标前景样本进行归类,最后通过对目标前景样本协方差矩阵特征值和特征向量的分析获得目标的尺度及方向参数,从而实现对视频运动目标的定位。实验结果表明,所提出的方法与原减法聚类定位方法相比可获得更合理的目标定位结果。 To overcome the problem that the common subtractive clustering object locating method could not acquire video object's scale and orientation parameters,an improved subtractive clustering object locating algorithm,that is called the scale and direction adaptive locating of video moving objects with subtractive clustering algorithm,is proposed. The proposed method first uses common subtractive clustering locating method to acquire video object position and moving object number of each frame. Then,the proposed algorithm uses fuzzy C-means clustering algorithm to further cluster the moving object foreground pixel samples. Finally,matrix analysis theory for calculating eigenvalues and eigenvectors of covariance matrix is applied to compute the object scale and orientation parameters. Experiment results show that the proposed object locating algorithm could obtain much more reasonable video object locating results.
出处 《光电工程》 CAS CSCD 北大核心 2010年第1期37-42,共6页 Opto-Electronic Engineering
基金 浙江省重大科技专项(优先主题工业项目)资助项目(2008C11108-1 2008C13076) 浙江省自然科学基金项目(Y1080883)
关键词 减法聚类 目标定位 目标检测 模糊C均值聚类 subtractive clustering object locating object detection fuzzy C-means clustering
  • 相关文献

参考文献7

二级参考文献31

  • 1Gian Luca Foresti. Object Recognition and Tracking for Remote Video Surveillance [J]. IEEE Transactions on Circuits and Systems for Video Technology, 1999, 9(7): 1045-1062.
  • 2Fatih Porikli. Real-Time Video Object Segmentation for MPEG Encoded Video Sequences [J]. SPIE Conference on Real-Time Imagining VIII, 2004, 5297: 195-203.
  • 3Changick Kim. Fast and Automatic Video Object Segmentation and Tracking for Content-Based Applications [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2002, 12(2): 122-129.
  • 4Jacinto C Nascimento. Performance Evaluation of Object Detection Algorithms for Video Surveillance [J]. IEEE Transactions on Multimedia, 2006, 8(4): 761-774.
  • 5Chiu S L. Fuzzy model identification based on cluster estimation [J]. Intelligent Fuzzy Systems, 1994, 2: 267-278.
  • 6Tao Yang, Stan Z Li, Quan Pan, et al. Real-Time and Accurate Segmentation of Moving Objects in Dynamic Scene [C]// International Multimedia Conference Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks. NewYork, NY, USA: ACM, 2004: 136-143.
  • 7Otsu N. A threshold selection method from gray-level histograms [J]. IEEE Transactions on System Man and Cyhemetic, 1979, 9(1): 62-66.
  • 8Grinias I, Tziritas G. A semi-automatic seeded region growing algorithm for video object localization and tracking [J]. Signal Processing: Image Communication, 2001, 16(10): 977-986.
  • 9FORESTI G L. Object recognition and tracking for remote video surveillance [J]. IEEE Transactions on Circuits and Systems, 1999, 9(7) :1045 - 1062.
  • 10PORIKLI F. Real-time video object segmentation for MPEG encoded video sequences [C]//SPIE Conference on Real-Time Imaging Ⅷ. San Jose: [s. n.], 2004, 5297:195 - 203.

共引文献18

同被引文献6

引证文献1

二级引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部