  The ROI (region-of-interest) based pooling method performs pooling operations
on the cropped ROI regions for various samples and has shown great success in
the object detection methods. It compresses the model size while preserving the
localization accuracy, thus it is useful in the visual tracking field. Though
being effective, the ROI-based pooling operation is not yet considered in the
correlation filter formula. In this paper, we propose a novel ROI pooled
correlation filter (RPCF) algorithm for robust visual tracking. Through
mathematical derivations, we show that the ROI-based pooling can be
equivalently achieved by enforcing additional constraints on the learned filter
weights, which makes the ROI-based pooling feasible on the virtual circular
samples. Besides, we develop an efficient joint training formula for the
proposed correlation filter algorithm, and derive the Fourier solvers for
efficient model training. Finally, we evaluate our RPCF tracker on OTB-2013,
OTB-2015 and VOT-2017 benchmark datasets. Experimental results show that our
tracker performs favourably against other state-of-the-art trackers.
