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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, 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, 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)

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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

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