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基于目标性权值度量的多示例学习目标跟踪

滑维鑫 慕德俊 郭达伟 刘航

滑维鑫, 慕德俊, 郭达伟, 等 . 基于目标性权值度量的多示例学习目标跟踪[J]. 北京航空航天大学学报, 2017, 43(7): 1364-1372. doi: 10.13700/j.bh.1001-5965.2016.0527
引用本文: 滑维鑫, 慕德俊, 郭达伟, 等 . 基于目标性权值度量的多示例学习目标跟踪[J]. 北京航空航天大学学报, 2017, 43(7): 1364-1372. doi: 10.13700/j.bh.1001-5965.2016.0527
HUA Weixin, MU Dejun, GUO Dawei, et al. Visual object tracking based on objectness measure with multiple instance learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(7): 1364-1372. doi: 10.13700/j.bh.1001-5965.2016.0527(in Chinese)
Citation: HUA Weixin, MU Dejun, GUO Dawei, et al. Visual object tracking based on objectness measure with multiple instance learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(7): 1364-1372. doi: 10.13700/j.bh.1001-5965.2016.0527(in Chinese)

基于目标性权值度量的多示例学习目标跟踪

doi: 10.13700/j.bh.1001-5965.2016.0527
基金项目: 

国家自然科学基金 61303224

国家自然科学基金 61672433

详细信息
    作者简介:

    滑维鑫  男, 博士研究生。主要研究方向:目标检测与跟踪、多目标优化

    慕德俊  男, 博士, 教授。主要研究方向:控制理论与应用、网络信息安全

    通讯作者:

    慕德俊, E-mail:mudejun@nwpu.edu.cn

  • 中图分类号: TP391;TP391.4

Visual object tracking based on objectness measure with multiple instance learning

Funds: 

National Natural Science Foundation of China 61303224

National Natural Science Foundation of China 61672433

More Information
  • 摘要:

    针对多示例学习(MIL)跟踪算法在包概率计算过程中对示例样本不加以区分导致分类器性能下降,及采用最大化似然函数选择相应的弱分类构造强分类增加了算法复杂度的问题,提出了一种基于目标性权值学习的多示例目标跟踪算法,该算法利用目标性测量每个示例样本对包概率的重要性,根据其目标性测量结果对每个正示例样本赋予相应的权值,从而判别性地计算包概率,提高跟踪精度。同时在弱分类器选择过程中,采用最大化弱分类器与似然函数概率内积的方法从弱分类器池中选择弱分器构造强分类器,减少算法的计算复杂度。通过对不同复杂场景下视频序列的跟踪,实验结果表明,本文所提出的目标性权值学习的多示例目标跟踪算法优于其对比算法,表现出较好的跟踪精度和鲁棒性能。

     

  • 图 1  正负样本示例及其NG特征

    Figure 1.  Positive and negative sample instances and their NG features

    图 2  1regularized SVM分类器正样本获取方法

    Figure 2.  Acquisition method of positive sample for ℓ1regularized SVM classifier

    图 3  本文算法流程图

    Figure 3.  Flowchart of proposed algorithm

    图 4  中心位置误差曲线

    Figure 4.  Center position error curves

    图 5  重叠率曲线

    Figure 5.  Overlap rate curves

    图 6  对遮挡、旋转、尺度及光照变化视频序列的跟踪结果

    Figure 6.  Tracking results for video sequences with occlusion, rotation, scale and illumination changes

    图 7  对遮挡、快速运动及复杂背景视频序列的跟踪结果

    Figure 7.  Tracking results for video sequences with occlusion, fast motion and background clutters

    表  1  测试视频序列的特点

    Table  1.   Characteristics of test video sequences

    视频序列 帧数 主要特点
    david1 471 遮挡、尺度、旋转及光照变化
    trellic 569 旋转、尺度及光照变化,复杂背景
    Shaking 365 光照、尺度变化,旋转及复杂背景
    Tiger2 365 遮挡,快速运动,复杂背景
    deer 71 运动模糊、形变、旋转及复杂背景
    carDark 393 光照变化及复杂背景
    下载: 导出CSV

    表  2  平均中心位置误差

    Table  2.   Average center position error

    视频序列 平均中心位置误差/pixel
    OAB[8] MIL[10] WMIL[12] 本文算法
    david1 27.74 21.07 19.97 15.18
    trellic 77.35 68.79 63.96 20.94
    Tiger2 40.87 42.69 39.67 22.59
    Shaking 144.67 14.55 25.80 19.47
    deer 13.61 15.69 34.83 6.21
    carDark 3.90 48.42 71.22 2.79
    平均值 51.35 35.20 42.58 14.53
    下载: 导出CSV

    表  3  平均重叠率

    Table  3.   Average overlap rate

    视频序列 重叠率
    OAB[8] MIL[10] WMIL[12] 本文算法
    david1 0.344 0.373 0.361 0.427
    trellic 0.136 0.264 0.235 0.501
    Tiger2 0.348 0.382 0.453 0.529
    Shaking 0.017 0.580 0.418 0.526
    deer 0.643 0.611 0.417 0.710
    carDark 0.741 0.153 0.089 0.762
    平均值 0.372 0.394 0.323 0.575
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-06-20
  • 录用日期:  2016-09-01
  • 刊出日期:  2017-07-20

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