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    • 摘要: 针对背景感知相关滤波器的空间正则化权重固定,不适应目标变化和增大搜索区域,易引入背景噪声导致滤波器判别力下降等问题,本文提出一种基于自适应空间正则化和畸变抑制的相关滤波跟踪算法。首先提取FHOG特征、CN特征和灰度特征以增强算法模型对目标的表达能力;其次,在目标函数中加入畸变抑制项来约束当前帧的响应图,增强滤波器的判别能力,以缓解滤波器模型退化问题;最后,在目标函数中加入自适应空间正则化项使空间正则化权重能够随着目标的变化而更新,使得滤波器能充分利用目标的多样性信息。本文在公开数据集OTB-2013、OTB-2015和VOT2016上进行实验,以对所提算法进行评估。实验结果表明:本文算法速度为20 f/s,距离精度和成功率等评估指标均优于对比算法,在遮挡、背景干扰、旋转变化等多种复杂场景下都有良好的鲁棒性。

       

      Abstract: This paper proposes a correlation filter tracking algorithm based on adaptive spatial regularization and aberrance repression aiming at the problem that the spatial regularization weight of the background-aware correlation filter is fixed and does not adapt to the change of the target, and the problem that enlarging search area may introduce background noise, decreasing the discrimination ability of filters. First, FHOG features, CN features, and gray features are extracted to enhance the algorithm's ability to express the target. Second, aberrance repression terms are added to the target function to constrain the response map of the current frame, and to enhance the filter's discrimination ability to alleviate the filter model degradation. Finally, adaptive spatial regularization terms are added to the objective function to make the spatial regularization weights being updated as the objective changes, so that the filter can make full use of the target's diversity information. This paper involves experiments on the public data sets OTB-2013, OTB-2015 and VOT2016 to evaluate the proposed algorithm. The experimental results show that the speed of the algorithm used in this paper is 20 frames/s, evaluation indicators such as distance accuracy and success rate are superior to comparison algorithms, and it has good robustness in a variety of complex scenarios such as occlusion, background interference, and rotation changes.