Correlation filters (CF) have sparked a lot of interest in visual tracking owing to their impressive performance. Most of previous correlation filter methods learn a filter from all features of the sample region. However, some features may be distractive, like those from occlusions and deformations, leading to tracking drift in the next frame. To mitigate this problem, we propose a novel region sparse constraint correlation filter (RSCF) to adaptively ignore those distractive features. The proposed method is formulated based on the elastic net model, and a binary mask is used to directly limit the sparsity of the filter values corresponding to the target region instead of the whole sample region. Besides, a context-aware term is integrated to enhance the discriminant ability of the filter. Last, an ADMM optimization algorithm is proposed to solve the model. Qualitative evaluations have been conducted on well-known benchmark, such as OTB-2013, OTB-2015, Temple-Color 128 and VOT2016. Experiment results demonstrate that the proposed tracker performs favorably against several state-of-the-art methods.

由于其出色的性能，相关滤波器（CF）引起了人们对视觉跟踪的极大兴趣。大多数以前的相关滤波器方法都从样本区域的所有特征中学习滤波器。但是，某些特征可能会分散注意力，例如来自遮挡和变形的那些特征，从而导致在下一帧中跟踪漂移。为了减轻这个问题，我们提出了一种新颖的区域稀疏约束相关滤波器（RSCF）来自适应地忽略那些分散的特征。该方法是基于弹性网络模型制定的，并使用二进制掩码直接限制与目标区域而不是整个样本区域相对应的滤波器值的稀疏性。此外，集成了上下文感知术语以增强过滤器的判别能力。持续，提出了ADMM优化算法对该模型进行求解。已对著名基准进行定性评估，例如OTB-2013，OTB-2015，Temple-Color 128和VOT2016。实验结果表明，所提出的跟踪器的性能优于几种最新方法。