期刊文献+

一种基于图模型的粒子滤波跟踪方法 被引量:5

Particle filter tracking algorithm based on graphical model
下载PDF
导出
摘要 针对复杂背景下目标被部分遮挡时的稳定跟踪问题,提出一种基于图模型的粒子滤波跟踪方法。该方法将图模型应用于粒子滤波之中。融合颜色和边缘特征建立目标的观测模型,构建粒子滤波框架。选取目标特征区域,将一个目标分成几个部分,每一部分作为图的一个顶点,建立图模型。最后,将图模型应用于粒子滤波,目标跟踪过程中,图模型中每一个部分的状态信息可以传送给其他部分。实验结果表明,当目标被部分遮挡的情况下,该方法能够估计出遮挡部分的状态,实现稳定的跟踪目标。 In complicated scene, in order to tackle to track stably objects whose part were temporally occluded, the paper presented a method for particle filter tracking algorithm based on graphical model. It incorporated graphical models into parti- cle filtering. Firstly, by the fusion of color and edge character, set up target observation model, and created particle filter framework. Then, selected target feature area, this method used not one region of the whole of the object but the multi-part re- gion of it if it was divided in some parts. It created graphical model by using the multi-part region. Finally, incorporated graphical models into particle filtering, in the process of target tracking, sent messages about the state of the parts to other parts in the graphical model. On the experiments of tracking people, as the result, when the tracked object has occluded part, the proposed method can infer the state of the occluded part, and track stably.
出处 《计算机应用研究》 CSCD 北大核心 2016年第2期590-593,共4页 Application Research of Computers
基金 国家教育部人文社会科学研究青年基金项目(13YJCZH251) 陕西省自然科学基础研究计划基金资助项目(2014JM8346) 陕西省教育厅科学研究计划项目(15JK1680)
关键词 图模型 粒子滤波 目标跟踪 信任传播 graphical model particle filter object tracking belief propagation
  • 相关文献

参考文献14

  • 1Lakshmi C,Revathí R,Hemalatha M.Video surveillance systems:a survey[J].International Journal of Computer Science Issues,2011,8(4):635-642.
  • 2Cristani M ,Raghavendra R ,Del Bue A.Human behavior analysis in video surveillance:a social signal processing perspective[J].Neurocomputing,2013,100(1):86-97.
  • 3Fouad B,Lynda D,Hichem S.Improved Mean-Shift integrating texture and color features for robust real time object tracking[J].The Visual Computer,2013,29(3):155-170.
  • 4Bouaynaya N,Schonfeld D.On the optimality of motion-based particle filtering[J].IEEE Trans on Circuits and Systems for Video Technology,2009,19(7):1068-1072.
  • 5Dore A,Soto M,Carlo S,et al.Bayesian tracking for video analytics[J].IEEE Signal Processing Magazine,2010,27(5):46-55.
  • 6杨宁,钱峰,朱瑞.基于遗传算法的改进粒子滤波算法[J].上海交通大学学报,2011,45(10):1526-1530. 被引量:14
  • 7Nummiaro K,Koller-Meier E,Gool L J V.An adaptive color-based particle filter[J].Image and Vision Computing,2003,21(1):99-110.
  • 8Wang Hanzi,Suter D,Schindler K,et al.Adaptive object tracking based on an effective appearance filter[J].IEEE Trans on Pattern Analysis and Machine Intelligence,2007,29(9):1661-1667.
  • 9Loza A,Mihaylova L,Bull D R,et al.Structural similarity-based object tracking in multimodality surveillance videos[J].Machine Vision and Applications,2009,20:71-83.
  • 10Xu Xinyu,Li Baoxin.Head tracking using particle filter with intensity gradient and color histogram[C]//Proc of IEEE International Confe-rence on Multimedia and Expo.[S.l.] :IEEE Press,2005:888-891.

二级参考文献11

共引文献13

同被引文献67

  • 1闫钧华,陈少华,艾淑芳,李大雷,段贺.基于Kalman预测器的改进的CAMShift目标跟踪[J].中国惯性技术学报,2014,12(4):536-542. 被引量:28
  • 2李霞.基于Mean-Shift算法的目标跟踪技术研究[J].自动化与仪器仪表,2016(4):20-22. 被引量:1
  • 3陈锻生,刘政凯.肤色检测技术综述[J].计算机学报,2006,29(2):194-207. 被引量:118
  • 4孙涛,谷士文,费耀平.基于PCA算法的人脸识别方法研究比较[J].现代电子技术,2007,30(1):112-114. 被引量:14
  • 5耿文东.编队目标跟踪综述[c]//第十届全国雷达学术年会.北京:国防工业出版社,2008:367-371.
  • 6Pratt W K.数字图像处理[M].邓鲁华,张延恒译.北京:机械工业出版社,2005:299-325.
  • 7张晶炜,熊伟,何友.基于数据压缩的多传感器多假设算法[J].北京航空航天大学学报,2007,33(12):1448-1451. 被引量:8
  • 8PENG Z H, SUN L, CHEN J, et al. Path Planning of Multiple UAVs L0w-Altitude Penetration Based on Improved Multi-Agent Coevolutionary Algorithm[C: //30th Chinese Control Conference, Yantai: IEEE, 2011:4056-4061. QIN Zhen, SHELTON C R. Improving Multi-Target Tracking via Social Groupingl-C://25th IEEE Confer- ence on Computer Vision and Pattern Recognition, Providence, Rhode Island: IEEE' 2012 : 1972-1978.
  • 9TRAN A, MANZANERA A. A Versatile Object Tracking Algorithm Combining Particle Filter and Generalised Hough Transform E C: // International Conference on Image Processing Theory, Tools and Applications, Orleans, France:IEEE, 2015:105-110.
  • 10ZHOU H, GAO Y, YUAN G, et al. Adaptive Multi- ple Cues Integration for Particle Filter Tracking[C']// IET International Radar Conference, Hangzhou:IET, 2015:1-6.

引证文献5

二级引证文献15

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