Yao Sui

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Target representation is a necessary component for a robust tracker. However, during tracking, many complicated factors may make the accumulated errors in the representation significantly large, leading to tracking drift. This paper aims to improve the robustness of target representation to avoid the influence of the accumulated errors, such that the(More)
Good tracking performance is in general attributed to accurate representation over previously obtained targets or reliable discrimination between the target and the surrounding background. In this work, we exploit the advantages of the both approaches to achieve a robust track-er. We construct a subspace to represent the target and the neighboring(More)
Representation method is critical to visual tracking. A robust representation describes the target accurately, leading to good tracking performance. In this work, a novel representation is proposed, which is designed to be simultaneously low-rank and joint sparse for the local patches within a target region. In this representation, the subspace structure is(More)
Single object tracking, in which a target is often initialized manually in the first frame and then is tracked and located automatically in the subsequent frames, is a hot topic in computer vision. The traditional tracking-by-detection framework, which often formulates tracking as a binary classification problem, has been widely applied and achieved great(More)
Correlation filtering based tracking model has received lots of attention and achieved great success in real-time tracking, however, the lost function in current correlation filtering paradigm could not reliably response to the appearance changes caused by occlusion and illumination variations. This study intends to promote the robustness of the correlation(More)
A fundamental component of modern trackers is an online learned tracking model, which is typically modeled either globally or locally. The two kinds of models perform differently in terms of effectiveness and robustness under different challenging situations. This work exploits the advantages of both models. A subspace model, from a global perspective , is(More)