Robust Visual Tracking Based on Spatial Reliability Constraint
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摘要: 针对复杂背景下目标容易发生漂移的问题，该文提出一种基于空间可靠性约束的目标跟踪算法。首先通过预训练卷积神经网络(CNN)模型提取目标的多层深度特征，并在各层上分别训练相关滤波器，然后对得到的响应图进行加权融合。接着通过高层特征图提取目标的可靠性区域信息，得到一个二值注意力矩阵，最后将得到的二值矩阵用于约束融合后响应图的搜索范围，范围内的最大响应值即为目标的中心位置。为了处理长时遮挡问题，该文提出一种基于首帧模板信息的随机选择更新策略。实验结果表明，该算法在应对相似背景干扰、遮挡、超出视野等多种场景均有良好的性能表现。Abstract: Because of the problem that the target is prone to drift in complex background, a robust tracking algorithm based on spatial reliability constraint is proposed. Firstly, the pre-trained Convolutional Neural Network (CNN) model is used to extract the multi-layer deep features of the target, and the correlation filters are respectively trained on each layer to perform weighted fusion of the obtained response maps. Then, the reliability region information of the target is extracted through the high-level feature map, a binary matrix is obtained. Finally, the obtained binary matrix is used to constrain the search area of the response map, and the maximum response value in the area is the target position. In addition, in order to deal with the long-term occlusion problem, a random selection model update strategy with the first frame template information is proposed. The experimental results show that the proposed algorithm has good performance in dealing with similar background interference, occlusion, and other scenes.
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Key words:
- Visual tracking /
- Spatial reliability constraint /
- Deep features /
- Correlation filter /
- Model update
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表 1 基于空间可靠性约束的鲁棒视觉跟踪算法
输入：图像序列I1, I2, ···, In，目标初始位置p0=(x0, y0)，目标初
始尺度s0=(w0, h0)。输出：每帧图像的跟踪结果pt=(xt, yt), st=(wt, ht)。 对于t=1, 2, ···, n, do： (1) 定位目标中心位置 (a) 利用前一帧目标位置pt–1确定第t帧ROI区域，并提取其
分层卷积特征；(b) 对于每一层的卷积特征，利用式(4)和式(5)计算其相关
响应图；(c) 利用式(6)对多个相关响应图进行融合，得到最终的相
关响应图；
(d)通过式(7)和式(8)提取空间可靠性区域图并将用于约束
响应图搜索范围；(e) 利用式(9)确定第t 帧中目标的中心位置pt。 (2) 确定目标最佳尺度 (a) 利用pt和前一帧目标尺度st–1进行多尺度采样，得到采样
图像集Is={$ I_{s_1},\ I_{s_2},\ ·\!·\!·,\ I_{s_m}$}；(b) 采用文献[14]中的尺度估计方法确定第t帧中目标的最佳
尺度st。(3) 模型更新 (a) 通过得到响应图计算最大响应值； (b) 依据响应值大小和式(10)—式(12)对滤波器进行更新。 结束 
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表 2 不同属性下算法的跟踪精度对比结果
算法 SV(60) OCC(45) IV(34) BC(27) DEF(42) MB(29) FM(37) IPR(46) OPR(57) OV(13) LR(8) 本文算法 0.827 0.799 0.855 0.872 0.801 0.813 0.800 0.879 0.844 0.756 0.870 HDT 0.811 0.753 0.803 0.855 0.817 0.764 0.800 0.851 0.804 0.663 0.749 HCF 0.800 0.748 0.805 0.857 0.788 0.772 0.788 0.863 0.807 0.680 0.778
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表 3 不同属性下算法的跟踪成功率对比结果
算法 SV(60) OCC(45) IV(34) BC(27) DEF(42) MB(29) FM(37) IPR(46) OPR(57) OV(13) LR(8) 本文算法 0.580 0.594 0.635 0.627 0.570 0.624 0.609 0.605 0.597 0.556 0.510 HDT 0.491 0.528 0.540 0.593 0.546 0.545 0.549 0.557 0.533 0.541 0.376 HCF 0.490 0.526 0.547 0.602 0.532 0.557 0.550 0.599 0.534 0.542 0.383
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表 4 算法各部分对跟踪性能影响对比实验
SRCT SRCT-S SRCT-R SRCT-S-R 成功率 0.624 0.618 0.610 0.603 跟踪精度 0.864 0.856 0.841 0.838
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