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电力场景下基于无人机视觉的运动目标追踪方法

冯雪 杜猛俊 向新宇 钱锦 张敏

冯雪, 杜猛俊, 向新宇, 等 . 电力场景下基于无人机视觉的运动目标追踪方法[J]. 北京航空航天大学学报, 2022, 48(4): 586-594. doi: 10.13700/j.bh.1001-5965.2020.0613
引用本文: 冯雪, 杜猛俊, 向新宇, 等 . 电力场景下基于无人机视觉的运动目标追踪方法[J]. 北京航空航天大学学报, 2022, 48(4): 586-594. doi: 10.13700/j.bh.1001-5965.2020.0613
FENG Xue, DU Mengjun, XIANG Xinyu, et al. UAV-vision-based moving target tracking scheme in electric scenario[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(4): 586-594. doi: 10.13700/j.bh.1001-5965.2020.0613(in Chinese)
Citation: FENG Xue, DU Mengjun, XIANG Xinyu, et al. UAV-vision-based moving target tracking scheme in electric scenario[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(4): 586-594. doi: 10.13700/j.bh.1001-5965.2020.0613(in Chinese)

电力场景下基于无人机视觉的运动目标追踪方法

doi: 10.13700/j.bh.1001-5965.2020.0613
基金项目: 

国网浙江省电力有限公司科技项目 5211HZ19014U

详细信息
    通讯作者:

    杜猛俊, E-mail: du_mengjun@zj.sgcc.com.cn

  • 中图分类号: TP37

UAV-vision-based moving target tracking scheme in electric scenario

Funds: 

Science and Technology Project of Zhejiang Power Co.Ltd. of State Grid Corporation of China 5211HZ19014U

More Information
  • 摘要:

    随着人工智能技术的发展,面向电力系统的运动目标追踪技术逐渐得到关注,现有方法虽有一定成效,但是大多基于固定摄像头的监控视频录制,不能灵活追踪运动目标,当运动目标离开摄像头视野时,存在运动目标丢失问题。为此,利用无人机设备,并基于深度学习和核相关滤波技术,提出了一个电力场景下基于无人机视觉的运动目标追踪方法(MTTS_UAV)。所提方法采用改进的目标追踪方法与目标检测方法相结合的方式来追踪运动目标隐患,并引入2种无人机飞行控制模块:启发式和数据驱动式,使得无人机的飞行速度和方向可以根据目标移动情况自适应地调节。在真实变电站的安全帽人员数据集上进行了大量实验,对所提方法的追踪效果进行评估,结果表明:所提方法在真实数据集上的平均像素误差(APE)和平均重叠率(AOR)分别可达到2.37和0.67,验证了方法的有效性。

     

  • 图 1  电力场景下基于无人机视觉的运动目标追踪方法结构

    Figure 1.  Framework of UAV-vision-based moving target tracking scheme in electric scenario

    图 2  参数敏感性实验结果

    Figure 2.  Sensitivity analysis w.r.t. different γ1, γ2 and δ

    表  1  平均像素误差结果统计

    Table  1.   Statistical results of average pixel error

    w 平均像素误差
    方法1 方法2 本文方法
    1 20.03 13.61 2.37
    2 2.64 2.66 2.37
    3 2.50 2.50 2.37
    4 2.41 2.31 2.37
    5 2.35 2.56 2.37
    下载: 导出CSV

    表  2  平均重叠率结果统计

    Table  2.   Statistical results of average overlap rate

    w 平均重叠率
    方法1 方法2 本文方法
    1 0.39 0.50 0.67
    2 0.65 0.65 0.67
    3 0.66 0.64 0.67
    4 0.67 0.67 0.67
    5 0.68 0.64 0.67
    下载: 导出CSV

    表  3  平均重启次数统计

    Table  3.   Statistics of average restart times

    w 平均重启次数
    方法2 本文方法
    1 55.00 36.70
    2 48.70 36.70
    3 50.10 36.70
    4 48.20 36.70
    5 46.20 36.70
    下载: 导出CSV

    表  4  平均连续追踪帧数统计

    Table  4.   Statistics of the number of average consecutive tracking frames

    w 平均连续追踪帧数
    方法2 本文方法
    1 38.20 41.00
    2 35.50 41.00
    3 33.90 41.00
    4 35.30 41.00
    5 36.90 41.00
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-11-03
  • 录用日期:  2020-11-12
  • 网络出版日期:  2022-04-20

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