期刊文献+

自适应核密度估计运动检测方法 被引量:11

Adaptive Kernel Density Estimation for Motion Detection
下载PDF
导出
摘要 提出了一种自适应的核密度估计(Kernel density estimation,KDE)运动检测算法.算法首先提出一种自适应前景、背景阈值的双阈值选择方法,用于像素分类.该方法用双阈值克服了单阈值分类存在的不足,阈值的选择能自适应进行,且能适应不同的场景.在此基础上,本文提出了基于概率的背景更新模型,按照像素的概率来更新背景,并利用帧间差分背景模型和KDE分类结果解决背景更新中的死锁问题,同时检测背景的突然变化.实验证明了所提出方法的适应性和可靠性. This paper proposed a method of adaptive kernel density estimation (KDE) for motion detection. To begin with, an approach for adaptive selecting thresholds of foreground and background was proposed. By using the two thresholds, the approach can overcome defects of using only one threshold. More importantly, these two thresholds can be selected automatically and they are independent of scenes. Meanwhile, a background model updated according to probability was also provided. The background model of inter-frame difference incorporated with results of KDE can solve deadlock situations in background model. It can also be used to detect suddenly changed background. Experimental results were given to demonstrate that the proposed algorithms are suitable and effective for motion detection.
出处 《自动化学报》 EI CSCD 北大核心 2009年第4期379-385,共7页 Acta Automatica Sinica
关键词 核密度估计 运动检测 自适戍背景/前景阈值 突变背景 Kernel density estimation (KDE), motion detection, adaptive background/foreground threshold, suddenly changed background
  • 相关文献

参考文献15

  • 1Piccardi M. Background subtraction techniques: a review. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics. Hague, Netherlands: IEEE, 2004. 3099-3104
  • 2Wren C R, Azarhayejani A, Darrell T, Pentland A P. Pfinder: real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 780-785
  • 3Lo B P L, Velastin S A. Automatic congestion detection system for underground platforms. In: Proceedings of International Symposium on Intelligent Multimedia, Video, and Speech Processing. Hong Kong, China: IEEE, 2001. 158-161
  • 4Chien S Y, Ma S Y, Chen L G. Efficient moving object segmentation algorithm using background registration technique. IEEE Transactions on Circuits and Systems for Video Technology, 2002, 12(7): 577-586
  • 5李刚,邱尚斌,林凌,曾锐利.基于背景差法和帧间差法的运动目标检测方法[J].仪器仪表学报,2006,27(8):961-964. 被引量:111
  • 6王晓梅,王养利,牛平宏.基于自适应背景模型的步态检测与识别[J].计算机应用研究,2006,23(11):258-260. 被引量:2
  • 7Colombari A, Fusiello A, Murino V. Segmentation and tracking of multiple video objects. Pattern Recognition, 2007, 40(4): 1307-1317
  • 8Stauffer C, Grimson W E L. Adaptive background mixture models for real-time tracking. In: Proceedings of the Computer Society on Computer Vision and Pattern Recognition. Fort Collins, USA: IEEE, 1999. 246-252
  • 9左军毅,潘泉,梁彦,张洪才,程咏梅.基于模型切换的自适应背景建模方法[J].自动化学报,2007,33(5):467-473. 被引量:15
  • 10Oliver N M, Rosario B, Pentland A P. A Bayesian computer vision system for modeling human interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 831-843

二级参考文献30

  • 1COLLINS R T,LIPTON A J,KANADE T.Introduction to the special section on video surveillance[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(8):745-746.
  • 2VERSAVEL J.Road safety through video detection[C].Proceedings of 1999 IEEE/IEEJ/JSA1 International Conference on Intelligent Transportation Systems,1999:753-757.
  • 3HARITAOGLU I,HARWOOD D,DAVIS,et al.W/SUP 4/:Who?When?Where?What?A real timesystem for detecting and tracking people[C].Proceedings of Third IEEE International Conference on Automatic Face and Gesture Recognition,1998:222-227.
  • 4HARITAOGLU I,HARWOOD DAVID,DAVIS L S.Real-time surveillance of people and their acticities[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(8).
  • 5TEKALPAM.Digital video processing[M].PrenticeHall,1995.
  • 6M P Murrray. Gait as a Total Pattern of Movement[J]. American Journal of Physical Medicine, 1967,46( 1 ) :290-333.
  • 7G Johansson. Visual Perception of Biological Motion and a Model for Its Analysis[ J ]. Perception and Psychophysics, 1973,14 (2) : 201-211.
  • 8J H Yoo, M S Nixon, C J Harris. Extracting Human Gait Signatures by Body Segment Properties [ D ]. UK : Department of Electronics and Computer Science University of Southampton.
  • 9J Little,J E Boyd. Recognizing People by Their Gait:The Shape of Motion [ J ]. Journal of Computer Vision Research, 1998,1 (2) :2- 32.
  • 10D Cunado, M S Nixon, J N Carter. Using Gait as a Biometric, via Phaseweighted Magnitude Pectual [ C ]. Lecture Notes in Computer Science Prec. of AVBPA' 97,1997.95-102.

共引文献125

同被引文献106

引证文献11

二级引证文献71

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