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基于稀疏子空间选择的在线目标跟踪

黄丹丹 孙怡

黄丹丹, 孙怡. 基于稀疏子空间选择的在线目标跟踪. 自动化学报, 2016, 42(7): 1077-1089. doi: 10.16383/j.aas.2016.c150493
引用本文: 黄丹丹, 孙怡. 基于稀疏子空间选择的在线目标跟踪. 自动化学报, 2016, 42(7): 1077-1089. doi: 10.16383/j.aas.2016.c150493
HUANG Dan-Dan, SUN Yi. Online Object Tracking via Sparse Subspace Selection. ACTA AUTOMATICA SINICA, 2016, 42(7): 1077-1089. doi: 10.16383/j.aas.2016.c150493
Citation: HUANG Dan-Dan, SUN Yi. Online Object Tracking via Sparse Subspace Selection. ACTA AUTOMATICA SINICA, 2016, 42(7): 1077-1089. doi: 10.16383/j.aas.2016.c150493

基于稀疏子空间选择的在线目标跟踪

doi: 10.16383/j.aas.2016.c150493
详细信息
    作者简介:

    黄丹丹大连理工大学信息与通信工程学院博士研究生.2007年获得长春理工大学电子信息工程学院电子信息科学与技术系学士学位.主要研究方向为视频序列中的目标检测与目标跟踪.E-mail:dlut_huang@163.com

    通讯作者:

    孙怡大连理工大学信息与通信工程学院教授.1986年获得大连理工大学电子系学士学位.主要研究方向为图像处理, 模式识别与无线通信.本文通信作者.E-mail:lslwf@dlut.edu.cn

Online Object Tracking via Sparse Subspace Selection

More Information
    Author Bio:

    Ph.D. candidate at the School of Information and Communication Engineering, Dalian University of Technology. She received her bachelor degree from Changchun University of Science and Technology in 2007. Her research interest covers object detection and object tracking

    Corresponding author: SUN Yi Professor at Dalian University of Technology. She received her bachelor degree from Dalian University of Technology in 1986. Her research interest covers image processing, pattern recognition, and wireless communication. Corresponding author of this paper
  • 摘要: 本文在粒子滤波框架下提出一种基于稀疏子空间选择的两步在线跟踪方法.在跟踪的第一步,利用稀疏子空间选择算法筛选出与目标状态相似性较高的候选区域,并将目标与背景间的过渡区域定义为单独的类别以降低目标发生漂移的可能;第二步则通过构建有效的观测模型计算候选区域与目标状态间的相似性,其中相似性函数综合考虑二者在整体和局部特征上的相似性,且将目标的原始状态和当前状态都作为参考,因此增强了观测模型的可靠性;最后利用最大后验概率估计目标状态.此外,该算法通过对目标数据的更新来适应目标的表观变化.实验结果表明该算法能有效处理目标跟踪中的遮挡、运动模糊、光流与尺度变化等问题,与当前流行的9种跟踪方法在多个测试视频上的对比结果验证了该算法的有效性.
  • 图  1  源数据(样本)示意图((a)采样得到的候选区域; (b)归一化的候选图像)

    Fig.  1  Sketch map of source data(samples) ((a) Candidates obtained by sampling; (b) Normalizedcandidates)

    图  2  目标数据(模板)示意图((a)模板分布示意图; (b)归一化的模板图像)

    Fig.  2  Sketch map of target data (templates) ((a) Sketchmap of template distributions; (b) Normalizedtemplates)

    图  3  目标数据(模板)和源数据(样本)聚类示意图((a)样本示意图; (b)模板以及聚类结果; (c)样本聚类结果)

    Fig.  3  Sketch map of target data (templates) clustering andsource data (samples) clustering ((a) Sketch map of samples; (b) Templates and clustering results; (c) Samples clusteringresults)

    图  4  粒子预判示意图((a)目标模板示意图; (b)与目标模板属于同一类别的样本示意图)

    Fig.  4  Sketch map of particles pre-filter ((a) Sketch mapof target templates; (b) Sketch map of samples that has the sameclasses with target templates)

    图  5  粒子预判算法对多余粒子的滤除性能

    Fig.  5  Performance of the particles pre-filter algorithm forfiltering the redundant particles

    图  6  目标存在遮挡时的跟踪结果

    Fig.  6  Tracking results when targets are occluded

    图  7  目标快速运动产生模糊时的跟踪结果

    Fig.  7  Tracking results when targets appearance is blurry because of quick movement

    图  8  光流发生变化时的跟踪结果

    Fig.  8  Tracking results when targets undergo illumination changes

    图  9  目标发生形变((a), (b))以及场景中存在相似区域((b), (c))时的跟踪结果

    Fig.  9  Tracking results when targets occur deforms ((a) and (b)) and results when there are similar regions in scenes ((b) and (c))

    图  10  所有跟踪方法在全部测试视频上的跟踪性能((a)平均中心误差曲线; (b)平均重合面积参数曲线)

    Fig.  10  Performance of all the tracking methods in test sequences ((a) Average center error curve; (b) Average overlap area parametric curve)

    图  11  OPE曲线((a)跟踪精度的统计曲线; (b)跟踪成功率的统计曲线)

    Fig.  11  One-pass evaluation curve ((a) Precision plot of OPE; (b) Success plots of OPE)

    表  1  有无粒子预判处理的跟踪结果对比

    Table  1  Tracking results comparing between the method with particle pre-filter and the method without particle pre-filter

    平均中心位置误差(pixel) 平均面积重合参数
    Singer1 Faceocc Dollar Owl Singer1 Faceocc Dollar Owl
    无预判 6.6 10.2 6.4 120.1 0.67 0.80 0.83 0.06
    有预判 3.6 5.1 5.5 28.8 0.85 0.93 0.85 0.47
    下载: 导出CSV

    表  2  跟踪结果的平均中心位置误差(像素)

    Table  2  Average center location errors of tracking results (pixel)

    视频序列 IVT VTD Frag MIL TLD L1 SCM DSSM LSK Ours
    Car11 2.2 30.1 40.1 44.7 17.6 33.7 2.1 2.0 73.3 1.9
    Faceocc 11.2 9.5 5.7 18.6 16.0 6.5 3.9 42.7 7.7 5.1
    Sylv 6.0 21.6 7.5 7.0 5.6 22.0 5.5 5.1 59.9 5.0
    Dollar 7.0 65.1 71.0 6.6 11.8 68.9 6.1 11.0 47.2 5.5
    Football 6.9 6.6 7.9 209.4 13.0 58.4 26.4 11.7 15.9 6.1
    Jumping 34.8 111.9 21.2 41.8 38.0 6.1 12.7 63.5 3.9
    Owl 114.7 190.7 40.6 292.1 33.1 32.2 50.7 230.7 28.8
    Stone 2.4 26.7 67.4 31.0 5.6 32.6 3.8 43.9 105.4 2.9
    Race 176.4 82.2 221.4 214.7 28.7 4.3 217.2 3.9
    Singer1 9.1 3.7 42.1 241.0 27.5 5.6 3.7 11.9 7.7 3.6
    下载: 导出CSV

    表  3  跟踪结果的平均面积重合误差(像素)

    Table  3  Average overlap scores of tracking results (pixel)

    视频序列 IVT VTD Frag MIL TLD L1 SCM DSSM LSK Ours
    Car11 0.80 0.38 0.06 0.17 0.36 0.46 0.81 0.81 0.0 0.82
    Faceocc 0.81 0.84 0.89 0.73 0.67 0.87 0.91 0.48 0.82 0.93
    Sylv 0.72 0.57 0.69 0.73 0.72 0.48 0.72 0.73 0.35 0.75
    Dollar 0.81 0.36 0.29 0.80 0.64 0.31 0.83 0.75 0.37 0.85
    Football 0.72 0.73 0.71 0.06 0.60 0.32 0.40 0.61 0.41 0.74
    Jumping 0.24 0.15 0.34 0.22 0.20 0.63 0.43 0.17 0.73
    Owl 0.15 0.06 0.40 0.08 0.46 0.47 0.28 0.05 0.47
    Stone 0.56 0.40 0.18 0.36 0.48 0.42 0.56 0.13 0.07 0.65
    Race 0.02 0.30 0.05 0.05 0.51 0.55 0.01 0.64
    Singer1 0.50 0.82 0.30 0.32 0.35 0.66 0.85 0.72 0.62 0.85
    下载: 导出CSV

    表  4  本文跟踪算法与对比跟踪算法的平均运行时间比较

    Table  4  Average running times comparing between the proposed method and other methods

    跟踪方法 IVT VTD Frag MIL TLD L1 SCM DSSM LSK Ours
    运行时间(s/f) 0.12 6.74 1.28 1.3 0.34 5.25 3.33 1.82 0.86
    下载: 导出CSV
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出版历程
  • 收稿日期:  2015-07-30
  • 录用日期:  2016-01-23
  • 刊出日期:  2016-07-01

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