Coarse-to-fine visual tracking with PSR and scale driven expert-switching

Abstract Correlation filters are playing an important role in state-of-the-art visual tracking. To achieve superior speed and great discriminative power, correlation filter based trackers are equipped with circulant matrix and fast Fourier transform (FFT), which implicitly use a large amount of samples to efficiently train the correlation filters and contribute significantly to model complexity. However, the number of training samples is significantly reduced when a target's size is small. Consequently, the performance of the resulting correlation filter based trackers may be inhibited. Moreover, how to cope with the scale variations and the tracker drift is still an open problem. In this paper, to address the above problems, we propose a coarse-to-fine tracker which integrates a kernelized correlation filter (KCF) based tracker with detection proposal and a multi-expert based tracker via a simple yet effective Peak to Sidelobe Ratio (PSR) and scale driven schema. Specifically, in the coarse level, the KCF based tracker with detection proposal is used to estimate a target's state in each frame. Then, the PSR and scale variations are analyzed according to the tracking results obtained by the simple KCF based tracker with detection proposal. In the fine level, the complicated multi-expert based tracker will be started for tracking when PSR or scale is lower than a threshold. Based on the simple yet effective PSR and scale driven expert-switching scheme, the proposed coarse-to-fine tracker can select the most reliable tracker in the tracking process. Extensive experimental results on the OTB-50 benchmark demonstrate the efficiency and effectiveness of the proposed tracking method.