In recent years, discriminative correlation filter (DCF) based algorithms have significantly advanced the state of the art in visual object tracking. The key to the success of DCF is an efficient discriminative regression model trained with powerful multi-cue features, including both hand-crafted and deep neural network features. However, the tracking performance is hindered by their inability to respond adequately to abrupt target appearance variations. This issue is posed by the limited representation capability of fixed image features. In this work, we set out to rectify this shortcoming by proposing a complementary representation of a visual content. Specifically, we propose the use of a collaborative representation between successive frames to extract the dynamic appearance information from a target with rapid appearance changes, which results in suppressing the undesirable impact of the background. The resulting collaborative representation coefficients are combined with the original feature maps using a spatially regularised DCF framework for performance boosting. The experimental results on several benchmarking datasets demonstrate the effectiveness and robustness of the proposed method, as compared with a number of state-of-the-art tracking algorithms.

中文翻译：

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基于协同表示的互补判别相关滤波器用于视觉对象跟踪

近年来，基于判别相关滤波器 (DCF) 的算法显着提高了视觉对象跟踪的最新技术水平。DCF 成功的关键是使用强大的多线索特征训练的高效判别回归模型，包括手工制作和深度神经网络特征。然而，跟踪性能因其无法对突然的目标外观变化做出充分响应而受到阻碍。这个问题是由固定图像特征的有限表示能力造成的。在这项工作中，我们着手通过提出视觉内容的补充表示来纠正这一缺点。具体来说，我们建议使用连续帧之间的协作表示从具有快速外观变化的目标中提取动态外观信息，这导致抑制背景的不良影响。使用空间正则化 DCF 框架将生成的协作表示系数与原始特征图相结合，以提高性能。与许多最先进的跟踪算法相比，在几个基准数据集上的实验结果证明了所提出方法的有效性和鲁棒性。