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Recently, part-based and support vector machines (SVM) based trackers haveshown favorable performance. Nonetheless, the time-consuming online trainingand updating process limit their real-time applications. In order to betterdeal with the partial occlusion issue and improve their efficiency, we proposea novel part-based structural support correlation filter tracking method, whichabsorbs the strong discriminative ability from SVM and the excellent propertyof part-based tracking methods which is less sensitive to partial occlusion.Then, our proposed model can learn the support correlation filter of each partjointly by a star structure model, which preserves the spatial layout structureamong parts and tolerates outliers of parts. In addition, to mitigate the issueof drift away from object further, we introduce inter-frame consistencies oflocal parts into our model. Finally, in our model, we accurately estimate thescale 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 performssuperiorly against several state-of-the-art trackers in terms of trackingaccuracy, speed and robustness.

Part-based Visual Tracking via Structural Support Correlation Filter