基于信息熵的Mean-shift目标跟踪
作者:
基金项目:

重庆市计算机网络与通信技术重点实验室项目(CY-CNCL-2008-02)

  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [10]
  • |
  • 相似文献 [20]
  • | | |
  • 文章评论
    摘要:

    视频目标跟踪在交通、军事等领域具有重要的应用价值。基于信息熵理论,提出了一种视频特征相关匹配的视频目标跟踪算法。首先引入信息熵概念,以信息熵描述视频目标特性,结合Mean-shift算法,针对不同的两个颜色空间RBG与HSV,用特征相关匹配法设计跟踪算法。试验结果表明,所提跟踪算法跟踪具有较好的实时性,取得较好的跟踪效果。

    Abstract:

    Video object tracking technology has great applied value in the fields of traffic and military. In this paper, a video matching object tracking algorithm, which is comentropy-based, is proposed. First of all, the concept of comentropy is introduced. Secondly, the feature of the video object is described by comentropy, and the algorithm which combined with mean-shift is designed with feature match in two different feature spaces, RGB and HSV. Finally, the experiment simulation is presented. The experiment results show that the proposed algorithm can track the object steady and real-time. This algorithm can achieve the desired effects and it is a practical algorithm.

    参考文献
    1 John ZM. An Information Theoretic Approach to Content Based Image Retrieval. Louisiana State University and Agricultural and Mechanical College. Phd. Thesis, 2000:45-62.
    2 Lian XK. A maximal fuzzy entropy based Gaussian clustering algorithm for tracking dim moving point targets in image sequences, International Conference on Computer Science and Software Engineering, CSSE 2008, v6:54-57.
    3 曾智勇,张学军,周利华,快速小波熵在图像检索中的应用.红外技术, 2005,27(6):469-472.
    4 Zhuang XH, Ostevold E, Haralick R. A differential equation approach to maximum entropy image reconstruction. IEEE Trans. Speech and Signal Processing, 1987,35(2):208-218.
    5 Di Zenzo S, Cinque L, Levialdi S. Image thresholding using fuzzy entropies. IEEE Transactions on. Man and Cybernetics, Part B, 1998,28(1):15-23.
    6 Song Y, Li QL, Sun FC. Shannon Entropy-Based Adaptive Fusion Particle Filter for Visual Tracking. Pattern Recognition, CCPR, 2009:1-5.
    7 田金文,苏康,柳健. 基于局部熵差的图像匹配方法——算法及计算机仿真.宇航学报, 1999,20(1):28-32.
    8 江和平,沈振康. 基于局部交叉熵的图像匹配跟踪算法.红外与激光工程, 2005,34(6):729-732.
    9 Bradski GR. Computer vision face tracking for use in a perceptual user interface. Intel Technology Journal, 1998:1-15.
    10 Comaniciu D, Meer P. Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Trans. Pattern Analysis and Machine Intelligence, 2002,24(5):603-619.
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

周尚波,蒋镇波,谢显中.基于信息熵的Mean-shift目标跟踪.计算机系统应用,2010,19(12):49-53

复制
分享
文章指标
  • 点击次数:2013
  • 下载次数: 3254
  • HTML阅读次数: 0
  • 引用次数: 0
历史
  • 收稿日期:2010-03-23
  • 最后修改日期:2010-05-04
文章二维码
您是第11226506位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京海淀区中关村南四街4号 中科院软件园区 7号楼305房间,邮政编码:100190
电话:010-62661041 传真: Email:csa (a) iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号