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复杂动背景下的“低小慢”目标检测技术

吴言枫 王延杰 孙海江 刘培勋

吴言枫, 王延杰, 孙海江, 刘培勋. 复杂动背景下的“低小慢”目标检测技术[J]. 中国光学(中英文), 2019, 12(4): 853-865. doi: 10.3788/CO.20191204.0853
引用本文: 吴言枫, 王延杰, 孙海江, 刘培勋. 复杂动背景下的“低小慢”目标检测技术[J]. 中国光学(中英文), 2019, 12(4): 853-865. doi: 10.3788/CO.20191204.0853
WU Yan-feng, WANG Yan-jie, SUN Hai-jiang, LIU Pei-xun. LSS-target detection in complex sky backgrounds[J]. Chinese Optics, 2019, 12(4): 853-865. doi: 10.3788/CO.20191204.0853
Citation: WU Yan-feng, WANG Yan-jie, SUN Hai-jiang, LIU Pei-xun. LSS-target detection in complex sky backgrounds[J]. Chinese Optics, 2019, 12(4): 853-865. doi: 10.3788/CO.20191204.0853

复杂动背景下的“低小慢”目标检测技术

doi: 10.3788/CO.20191204.0853
基金项目: 

国家自然科学基金青年基金项目 61602432

详细信息
    作者简介:

    吴言枫(1992-), 男, 吉林长春人, 博士研究生, 2015年于吉林大学获得学士学位, 主要从事数字图像处理、计算机视觉方面的研究。E-mail:wuyfciomp@yahoo.com

    王延杰(1963—),男,吉林长春人,研究员,博士生导师,主要从事实时图像处理、电视跟踪和自动目标识别技术的研究。E-mail:wangyj@ciomp.ac.cn

    孙海江(1981-), 男, 吉林辉南人, 副研究员, 主要从事目标识别与跟踪技术及高清视频图像增强显示方面的研究。E-mail:sunhaijiang@126.com

    刘培勋(1986-), 男, 吉林榆树人, 助理研究员, 博士, 主要研究方向为数字图像处理及机器视觉。E-mail:liupx@ciomp.ac.cn

  • 中图分类号: TP394.1;TH691.9

LSS-target detection in complex sky backgrounds

Funds: 

the National Natural Science Foundation of China Youth Science Foundation Project 61602432

More Information
  • 摘要: 为了在复杂天空背景下检测出低空慢速小目标,本文研究了“低小慢”目标的视觉显著性区域特征,融合扫描线填充算法,提出了一种动态背景下“低小慢”目标自适应实时检测技术。首先,根据图像的亮度对比度获取显著性图。接着,使用形态学梯度提取显著性特征,通过三帧差分算法得到种子点。然后,使用扫描线填充算法进行生长,结合提出的自适应双高斯算法分割出前景。最后,根据候选目标的面积占比变化、质心距离变化、宽高比差异剔除虚假目标,完成检测。为了验证算法的有效性,本文选取了7组复杂天空背景的视频序列进行测试,并与其他优秀检测算法进行了对比。结果表明,本文提出的算法对运动目标检测的平均运行时间为0.040 9 s,平均检测准确率为89.97%,相比于其他算法的平均运算时间减少了0.35 s,检测的平均准确率提高了24.5%。算法在复杂背景下具有较好的稳定性和较强的鲁棒性。

     

  • 图 1  “低小慢”目标的特征分析图像

    Figure 1.  Feature analysis of the LSS-target image

    图 2  视觉显著性结果

    Figure 2.  Visual saliency results

    图 3  梯度差分算法原理图

    Figure 3.  Schematic diagram of gradient difference algorithm

    图 4  梯度差分结果

    Figure 4.  Gradient difference results

    图 5  扫描线填充算法流程图

    Figure 5.  Flow chart of scan line filling algorithm

    图 6  双高斯函数拟合直方图结果

    Figure 6.  Double Gauss function fitting histogram results

    图 7  自适应阈值分割结果

    Figure 7.  Adaptive threshold segmentation results

    图 8  对比实验结果

    Figure 8.  Comparison of experimental results for different algorithms

    图 9  本文算法检测结果

    Figure 9.  Detection results by proposed algorithm

    表  1  剔除虚警流程

    Table  1.   Eliminating false alarm process

    input:候选目标集合R
    output:目标集合T
    1.ti={ri}, T={ϕ} Δ初始化
    2. For i=1, riR
    3. if
    4. Δ更新目标集合
    5. else
    6. Texist=T Δ剔除虚警
    7. End for
    下载: 导出CSV

    表  2  实验中的测试视频

    Table  2.   Test sequences in our experiments

    视频 帧数 SCR
    Video 1 76 <1
    Video 2 69 1~1.5
    Video 3 249 1.5~2
    Video 4 90 2~3
    Video 5 22 >3
    Video 6 64 >3
    Video 7 101 >3
    下载: 导出CSV

    表  3  检测准确率Pd及虚警率Pfa

    Table  3.   Detection accuracy and false alarm rate (%)

    视频 准确率 虚警率
    Vibe PBAS Ours Vibe PBAS Ours
    Video 1 26.3 65. 8 81.6 77.2 35.8 5.1
    Video 2 30.4 72.5 87.0 82.4 76.5 4.6
    Video 3 32.1 80.3 84.3 65.3 11.2 0.9
    Video 4 23.3 80 87.8 86.5 3.5 0.3
    Video 5 45. 5 90.9 100 40.7 4.6 0
    Video 6 62.5 78.1 100 33.5 4.8 0
    Video 7 59.4 84.2 89.1 20.9 6.5 6.2
    下载: 导出CSV

    表  4  算法的时间复杂度

    Table  4.   Average time consumption and total time consumption of the proposed algorithm

    视频 帧数 平均耗时(s/frame) 总耗时/s
    Vibe PBAS Ours Vibe PBAS Ours
    Video 1 76 0.362 0.396 0.042 27.512 30.096 3.05
    Video 2 69 0.346 0.389 0.048 24.081 26.841 3.31
    Video 3 249 0.372 0.412 0.047 96.682 102.588 11.61
    Video 4 90 0.329 0.386 0.046 29.61 34.74 4.102
    Video 5 22 0.349 0.391 0.045 7.678 8.602 0.988
    Video 6 64 0.354 0.397 0.043 22.656 25.408 2.764
    Video 7 101 0.371 0.403 0.047 37.471 40.703 4.770
    下载: 导出CSV
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  • 收稿日期:  2018-03-14
  • 修回日期:  2018-04-04
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