【摘要】 基于支持向量相关滤波器(Support Correlation Filters,SCF)的目标跟踪方法存在严重的样本边界不连续问题,因此模型判别能力受到严重限制。本文将空间正则化项引入到SCF中,提出了基于空间正则化约束的支持向量相关滤波器(Spatially Regularized SCF,SRSCF)模型。相比于SCF,SRSCF不仅可以借助更大的图像区域进行模型学习,同时也能缓解样本的边界不连续问题对模型学习的负面影响,由此得到判别能力更强的模型。此外,本文提出了一种ADMM(Alternating Direction Method of Multiplier)算法求解SRSCF模型,其中每个子问题具有解析解。实验结果表明,相较于SCF,SRSCF能够有效地提升跟踪精度,同时仅增加较少的计算开销。更多还原

【Abstract】 The existing support correlation Filters(SCF) methods suffer from unwanted boundary discontinuity problem of samples,resulting in the degraded CF models.To address this,this paper incorporates the spatial regularization term into the SCF method,and proposes the spatially regularized SCF(dubbed SRSCF) model.In comparison to SCF,SRSCF can leverage larger image regions during model learning,and also alleviate the negative impacts of boundary discontinuous samples on model training,thereby leading to more discriminative CF models.In addition,an ADMM algorithm is proposed to solve the proposed SRSCF model,in which each sub-problem has closed-form solution.Experimental results show that SRSCF can achieve better performance than the SCF models,and only need less additional computational overhead.更多还原