  Recently, part-based and support vector machines (SVM) based trackers have
shown favorable performance. Nonetheless, the time-consuming online training
and updating process limit their real-time applications. In order to better
deal with the partial occlusion issue and improve their efficiency, we propose
a novel part-based structural support correlation filter tracking method, which
absorbs the strong discriminative ability from SVM and the excellent property
of part-based tracking methods which is less sensitive to partial occlusion.
Then, our proposed model can learn the support correlation filter of each part
jointly by a star structure model, which preserves the spatial layout structure
among parts and tolerates outliers of parts. In addition, to mitigate the issue
of drift away from object further, we introduce inter-frame consistencies of
local parts into our model. Finally, in our model, we accurately estimate the
scale changes of object by the relative distance change among reliable parts.
The extensive empirical evaluations on three benchmark datasets: OTB2015,
TempleColor128 and VOT2015 demonstrate that the proposed method performs
superiorly against several state-of-the-art trackers in terms of tracking
accuracy, speed and robustness.
