A New Method of Anti-interference Matching Under Foreground Constraint for Target Tracking
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摘要: 传统模型匹配跟踪方法没有充分考虑目标与所处图像的关系，尤其在复杂背景下，发生遮挡时易丢失目标.针对上述问题，提出一种前景约束下的抗干扰匹配（Anti-interference matching under foreground constraint，AMFC）目标跟踪方法.该方法首先选取图像帧序列前m帧进行跟踪训练，将每帧图像基于颜色特征分割成若干超像素块，利用均值聚类组建簇集合，并通过该集合建立判别外观模型；然后，采用EM（Expectation maximization）模型建立约束性前景区域，通过基于LK（Lucas-Kanade）光流法框架下的模型匹配寻找最佳匹配块.为了避免前景区域中相似物体的干扰，提出一种抗干扰匹配的决策判定算法提高匹配的准确率；最后，为了对目标的描述更加准确，提出一种新的在线模型更新算法，当目标发生严重遮挡时，在特征集中加入适当特征补偿，使得更新的外观模型更为准确.实验结果表明，该算法克服了目标形变、目标旋转移动、光照变化、部分遮挡、复杂环境的影响，具有跟踪准确和适应性强的特点.Abstract: The relation between a moving target and its image has not been fully considered in traditional model-matching tracking methods. The tracking drift problem may frequently occur when the target is occluded under a complex background. In this paper, a novel target tracking method, anti-interference matching under foreground constraint (AMFC), is proposed to solve this kind of problem. First, the method selects several initial frames from a vedio sequence for tracking training. Each of these frames is divided into several super-pixel blocks based on its color feature. These super-pixel blocks are combined into cluster sets by a mean shift algorithm to construct a discrimination appearance model. Then, a constrained foreground region is established using the expectation maximization (EM) model and a matching process is conducted based on the Lucas-Kanade (LK) optical flow method in order to select the optimum matching block. A decision-making algorithm is introduced to avoid the interference caused by similar targets in the foreground region, so as to increase the accuracy of the matching process. Moreover, in order to provide a more accurate target representation, an algorithm for appearance model online-updating is proposed. When a severe occlusion occurs, this algorithm can append appropriate feature compensations to the feature sets to improve the accuracy of the appearance model. Experimental results indicate that the proposed approach can provide superior tracking accuracy and adaptability, especially in the context of target deformation, target rotational movements, illumination changes, partial occlusion, and complex background.
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图 3 $\sigma$值与平均中心误差之间的关系
Fig. 3 The relationship between the $\sigma$ value and average center error
图 8 11种跟踪算法在12组图像序列中的跟踪结果
Fig. 8 Tracking results of the 11 algorithms in the 12 image sequences
表 1 实验图像序列信息
Table 1 The information of the test image sequences
图像序列 光照变化 遮挡 形变 复杂背景 旋转 Girl $\surd$ $\surd$ Deer $\surd$ $\surd$ Bird2 $\surd$ $\surd$ Football $\surd$ $\surd$ Lemming $\surd$ $\surd$ $\surd$ $\surd$ Woman $\surd$ $\surd$ Bolt $\surd$ $\surd$ $\surd$ CarDark $\surd$ David1 $\surd$ $\surd$ $\surd$ David2 $\surd$ Singer1 $\surd$ $\surd$ $\surd$ Basketball $\surd$ $\surd$ $\surd$ $\surd$ 
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表 2 不同跟踪算法的平均中心误差
Table 2 Average center errors of different tracking algorithms
图像序列 ASLA FRAG SCM VTD L1APG CT OAB TLD LOT SPT AMFC Girl 36.76 24.27 $\mathbf{3.47}$ 8.64 25.51 19.45 4.68 7.66 20.28 4.73 4.76 Deer $\mathbf{6.74}$ 87.64 37.62 7.93 38.76 52.13 16.77 25.34 29.44 28.76 11.92 Bird2 20.12 14.96 11.53 46.24 25.44 37.87 26.16 10.23 47.61 $\mathbf{6.32}$ 6.97 Football 16.62 15.38 9.64 13.57 11.31 17.44 9.28 13.82 7.15 11.27 $\mathbf{6.28}$ Lemming 47.52 112.63 75.43 60.73 142.28 37.82 73.74 32.68 14.48 $\mathbf{9.41}$ 11.39 Woman 76.57 118.28 22.24 107.69 115.57 104.58 30.94 137.47 118.41 23.36 $\mathbf{21.71}$ Bolt 62.42 73.62 9.37 49.17 132.48 38.74 129.56 141.24 17.62 15.23 $\mathbf{8.44}$ CarDark 5.30 6.23 6.32 28.72 $\mathbf{3.44}$ 18.70 39.86 21.36 24.18 21.58 8.35 David1 $\mathbf{3.57}$ 84.41 4.38 48.95 5.76 9.69 31.26 8.92 37.84 23.29 9.36 David2 8.94 67.51 6.72 3.51 $\mathbf{3.23}$ 69.83 36.34 6.73 3.97 8.48 9.21 Singer1 45.62 57.83 17.85 12.53 49.84 31.07 36.27 22.17 16.64 $\mathbf{10.14}$ 10.25 Basketball 106.62 18.42 116.24 9.48 84.47 79.41 37.13 95.46 127.83 12.38 $\mathbf{7.73}$ 平均 36.40 56.77 26.73 33.10 53.17 43.06 39.33 43.59 38.79 14.58 9.70 注：粗体为最优结果.
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表 3 不同跟踪算法的跟踪重叠率
Table 3 Tracking overlap ratio of different tracking algorithms
图像序列 ASLA FRAG SCM VTD L1APG CT OAB TLD LOT SPT AMFC Girl 0.31 0.41 $\mathbf{0.74}$ 0.69 0.39 0.29 0.73 0.56 0.43 0.71 0.73 Deer $\mathbf{0.69}$ 0.11 0.47 0.63 0.45 0.34 0.56 0.41 0.50 0.53 0.61 Bird2 0.66 0.70 0.74 0.41 0.63 0.44 0.58 0.80 0.43 $\mathbf{0.84}$ 0.83 Football 0.61 0.63 0.71 0.65 0.68 0.62 0.68 0.65 0.73 0.69 $\mathbf{0.75}$ Lemming 0.71 0.43 0.53 0.57 0.39 0.74 0.56 0.77 0.83 $\mathbf{0.86}$ 0.86 Woman 0.27 0.14 0.59 0.15 0.15 0.17 0.48 0.12 0.14 0.59 $\mathbf{0.61}$ Bolt 0.53 0.47 0.76 0.55 0.23 0.58 0.20 0.16 0.72 0.73 $\mathbf{0.77}$ CarDark $\mathbf{0.82}$ 0.82 0.81 0.43 $\mathbf{0.82}$ 0.72 0.38 0.46 0.44 0.43 0.79 David1 $\mathbf{0.83}$ 0.23 0.82 0.53 0.80 0.77 0.57 0.79 0.55 0.62 0.76 David2 0.68 0.21 0.69 0.73 $\mathbf{0.74}$ 0.02 0.33 0.69 0.73 0.68 0.64 Singer1 0.58 0.55 0.73 0.74 0.57 0.64 0.63 0.69 0.72 $\mathbf{0.76}$ 0.76 Basketball 0.19 0.63 0.17 0.67 0.25 0.27 0.59 0.21 0.14 0.68 $\mathbf{0.69}$ 平均 0.57 0.45 0.65 0.56 0.51 0.47 0.52 0.53 0.53 0.68 0.73
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表 4 不同跟踪算法的平均运行速度
Table 4 Average running speeds of different tracking algorithms
图像序列 ASLA FRAG SCM VTD L1APG CT OAB TLD LOT SPT AMFC Girl 5.31 6.32 0.65 2.74 1.76 38.21 17.51 26.84 0.79 0.47 3.68 Deer 6.24 4.78 0.97 2.67 1.64 31.63 14.72 27.17 0.83 0.41 3.56 Bird2 5.74 5.43 0.67 2.58 1.49 27.06 9.94 26.58 0.65 0.56 3.09 Football 6.15 5.68 0.61 3.14 1.61 36.73 19.67 26.63 0.93 0.76 3.41 Lemming 6.78 6.27 0.69 2.77 1.75 28.15 10.46 26.70 0.71 0.37 3.39 Woman 8.45 6.41 0.57 2.16 1.53 32.42 11.31 26.32 0.66 0.43 4.37 Bolt 7.04 3.97 0.46 2.21 1.63 27.18 8.66 24.74 0.61 0.29 3.06 CarDark 7.23 4.23 0.48 2.49 1.74 26.79 10.35 25.13 0.59 0.36 2.95 David1 7.84 4.48 0.53 3.47 2.03 34.22 14.75 26.47 0.67 0.54 3.86 David2 5.87 5.25 0.48 2.68 1.44 36.36 16.68 25.89 0.73 0.66 2.97 Singer1 5.31 4.96 0.52 2.91 1.73 28.19 10.43 26.31 0.71 0.43 3.42 Basketball 7.91 6.23 0.89 2.34 2.04 25.81 9.09 24.53 0.62 0.34 2.89
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