ROI Pooled Correlation Filters for Visual Tracking
This work addresses robust visual tracking for applications like surveillance or autonomous systems, but it is incremental as it adapts an existing pooling method to a new context.
The paper tackled the problem of integrating ROI-based pooling into correlation filters for visual tracking, proposing the RPCF algorithm which achieved favorable performance against state-of-the-art trackers on benchmark datasets like OTB-2013, OTB-2015, and VOT-2017.
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.