留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

复杂背景灰度图像下的多特征融合运动目标跟踪

江山 张锐 韩广良 孙海江

江山, 张锐, 韩广良, 孙海江. 复杂背景灰度图像下的多特征融合运动目标跟踪[J]. 中国光学(中英文), 2016, 9(3): 320-328. doi: 10.3788/CO.20160903.0320
引用本文: 江山, 张锐, 韩广良, 孙海江. 复杂背景灰度图像下的多特征融合运动目标跟踪[J]. 中国光学(中英文), 2016, 9(3): 320-328. doi: 10.3788/CO.20160903.0320
JIANG Shan, Zhang Rui, HAN Guang-liang, SUN Hai-jiang. Moving object tracking based on multi-feature fusion in the complex background gray image[J]. Chinese Optics, 2016, 9(3): 320-328. doi: 10.3788/CO.20160903.0320
Citation: JIANG Shan, Zhang Rui, HAN Guang-liang, SUN Hai-jiang. Moving object tracking based on multi-feature fusion in the complex background gray image[J]. Chinese Optics, 2016, 9(3): 320-328. doi: 10.3788/CO.20160903.0320

复杂背景灰度图像下的多特征融合运动目标跟踪

doi: 10.3788/CO.20160903.0320
基金项目: 

国家自然科学基金资助项目 No.61172111

详细信息
    通讯作者:

    江山(1986-),男,吉林长春人,硕士,助理研究员,2010年、2013年于吉林大学分别获得学士、硕士学位,主要从事高速目标跟踪处理方面的研究。E-mail:617798169@qq.com

  • 中图分类号: TP391

Moving object tracking based on multi-feature fusion in the complex background gray image

Funds: 

Supported by National Natural Science Foundation of China No.61172111

  • 摘要: 为解决低对比度、低信噪比、目标旋转、缩放等非理想状态给跟踪算法的研究带来的诸多困难,本文提出灰度图像多特征融合目标跟踪算法,保证在满足工程实践需要的条件下,能够对目标进行稳定的跟踪。算法首先对灰度图像利用Sobel算子求出梯度特征,将XY双方向的梯度特征与灰度特征相融合得到新特征,新特征在核密度函数下对低对比度,目标轮廓形状变化较大的情况有较高的适应性和稳定性,再利用背景建模的方法对提取的运动目标区域进行加权,降低非跟踪目标的权值,最后对融合后的加权特征目标利用改进MeanShift算法进行跟踪。通过大量的实验表明,该算法适应目标和背景的复杂变化,并且具有较强的鲁棒性,基本满足在复杂背景灰度图像下目标跟踪的工程实际需求。

     

  • 图 1  MeanShift迭代收敛过程

    Figure 1.  Iterative convergence process of MeanShift algorithm

    图 2  Epanechnikov核函数曲面

    Figure 2.  Epanechnikov kernel surface

    图 3  待测模板图像

    Figure 3.  Image of template to be measured

    图 4  灰度特征图像核密度特征曲面

    Figure 4.  Gray image kernel surface

    图 5  多特征融合图像核密度特征曲面

    Figure 5.  Multi-feature fusion kernel surface

    图 6  微软通用视频集本文跟踪算法和传统算法对比分析

    Figure 6.  Comparison analysis between our algorithms and traditional one in Microsoft video set

    图 7  自制视频集本文跟踪算法和传统算法对比分析

    Figure 7.  Comparison analysis between our algorithms and traditional one in self-made video set

    图 8  微软通用视频集本文跟踪算法和传统算法对比分析(在复杂场景下)

    Figure 8.  Comparison analysis between our algorithms and traditional one in self-made video set(in a complex background)

  • [1] 高文,朱明,贺柏根,等.目标跟踪技术综述[J].中国光学,2014,7(3):365-375.

    GAO W,ZHU M,HE B G,et al. Overview of target tracking technology[J]. Chinese Optics,2014,7(3):365-375.(in Chinese)
    [2] 刘扬,张云峰,董月芳.复杂背景下抗遮挡的运动目标跟踪算法[J].液晶与显示,2010,25(6):890-895.

    LIU Y,ZHANG Y F,DONG Y F. Anti-occlusion algorithm of tracking moving object in clutter background[J]. Chinese J. Liquid Crystals and Displays,2010,25(6):890-895.(in Chinese)
    [3] 宋策,张葆,尹传历.适于机载环境对地目标跟踪的粒子滤波设计[J].光学 精密工程,2014,4(22):1037-1047.

    SONG C,ZHANG B,YIN CH L. Particle filter design for tracking ground targets in airborne environment[J]. Opt. Precision Eng.,2014,4(22):1037-1047.(in Chinese)
    [4] 李静宇,王延杰.基于子空间的目标跟踪算法研究[J].液晶与显示,2014,4(29):617-622.

    LI J Y,WANG Y J. Subspace based target tracking algorithm[J]. Chinese J. Liquid Crystals and Displays,2014,4(29):617-622.(in Chinese)
    [5] NING J F,ZHANG L,ZHANG D,et al. Scale and orientation adaptive meanshift tracking[J]. IET Computer Vision,2012,6(1):52-61.
    [6] 王铭明,陈涛,王建立,等.Mean-shift跟踪算法及其在光电跟踪系统中的应用[J].中国光学,2014,7(2):332-338.

    WANG M M,CHEN T,WANG J L,et al. Mean-shift tracking algorithm and its application in optoelectronic tracking system[J]. Chinese Optics,2014,7(2): 332-338.(in Chinese)
    [7] 王田,刘伟宁,韩广良,等.基于改进MeanShift的目标跟踪算法[J].液晶与显示,2012(3):396-400.

    WANG T,LIU W N,HAN G L,et al. Target tracking algorithm based on improved meanshift[J]. Chinese J. Liquid Crystals and Displays,2012(3):396-400.(in Chinese)
    [8] 闫辉,许廷发,吴青青,等.多特征融合匹配的多目标跟踪[J].中国光学,2013,6(2):163-170.

    YAN H,XU T F,WU Q Q,et al. Multi-object tracking based on multi-feature joint matching[J]. Chinese Optics,2013,6(2):163-170.(in Chinese)
    [9] 郭敬明,何昕,魏仲慧.基于在线支持向量机的Meanshift彩色图像跟踪[J].液晶与显示,2014,1(29):120-128.

    GUO J M,HE X,WEI ZH H. New mean shift tracking for color image based on online support vector machine[J]. Chinese J. Liquid Crystals and Displays,2014,1(29):120-128.(in Chinese)
    [10] COMANICIU D,RAMESH V,MEER P. Kernel-Based object tracking[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence,2003,25(2):564-577.
    [11] DECARLO D,METAXAS D. Optical flow constraints on deformable models with applications to face tracking[J]. In ternational J. Computer Vision,2000,38(2):99-127
    [12] LI X H,ZHANG T Y,SHEN X D,et al. Object tracking using an adaptive Kalman filter combined with mean shift[J]. SPIE J. Optical Engineering,2010,49(2):020503-3.
    [13] 文志强,蔡自兴.目标跟踪中巴氏系数误差的分析及其消除方法[J].计算机学报,2008,31(7):1165-1173.

    WEN ZH Q,CAI Z X. Errors of bhattacharyya coefficient and its reduction in object tracking[J]. Chinese J. Computers,2008,31(7):1165-1173.(in Chinese)
    [14] AHMAD S,KHATTAK,GULLISTAN T,et al. Integration of mean-shift and particle filter:a survey[C]. 12th International Conference on Frontiers of Information Technology. Islamic:FIT,2014:286-291.
    [15] 郭巳秋,许廷发,王洪庆,等.改进的粒子群优化目标跟踪方法[J].中国光学,2014,7(5):759-767.

    GUO S Q,XU T F,WANG H Q,et al. Object tracking method based on improved particle swarm optimization[J]. Chinese Optics,2014,7(5):759-767.(in Chinese)
    [16] JIA W H,YING Y Y. Multi-iterative tracking method using meanshift based on kalman filter[C]. International Conference on Signal Processing,Communications and Computing. China:ICSPCC,2014:22-27.
    [17] RASHID M,RAB N,NAVEED I R. Occlusion handling in meanshift tracking using adaptive window normalized cross correlation[C]. International Bhurban Conference on Applied Sciences and Technology. Islamic:IBCAST,2014:126-129.
    [18] VEZZANI R,GRANA C,CUCCHIARA R. Probabilistic people tracking with appearance models and occlusion[J]. Pattern Recognition Letters,2011,32(6):867-877.
    [19] 袁春兰,熊宗龙,周雪花,等.基于Sobel算子的图像边缘检测研究[J].激光与红外,2009,39(1):85-87.

    YUAN CH L,XIONG Z L,ZHOU X H,et al. Study of infrared image edge detection based on sobel operator[J]. Laser and Infrared,2009,39(1):85-87.(in Chinese)
  • 加载中
图(8)
计量
  • 文章访问数:  1564
  • HTML全文浏览量:  400
  • PDF下载量:  1054
  • 被引次数: 0
出版历程
  • 收稿日期:  2015-11-23
  • 修回日期:  2016-01-18
  • 刊出日期:  2016-01-25

目录

    /

    返回文章
    返回

    重要通知

    2024年2月16日科睿唯安通过Blog宣布,2024年将要发布的JCR2023中,229个自然科学和社会科学学科将SCI/SSCI和ESCI期刊一起进行排名!《中国光学(中英文)》作为ESCI期刊将与全球SCI期刊共同排名!