Faster Spatially Regularized Correlation Filters for Visual Tracking

TL;DR

This paper introduces FSRDCF, an improved correlation filter method for visual tracking that maintains high accuracy while significantly increasing computational efficiency by exploiting circulant structures in the Fourier domain.

Contribution

The paper proposes FSRDCF, which fully utilizes circulant structures in both spatial and Fourier domains, resulting in faster tracking without sacrificing accuracy compared to SRDCF.

Findings

Achieves equivalent tracking performance to SRDCF on benchmark datasets.

More than twice faster running speed than SRDCF.

Over three times shorter start-up time than SRDCF.

Abstract

Discriminatively learned correlation filters (DCF) have been widely used in online visual tracking filed due to its simplicity and efficiency. These methods utilize a periodic assumption of the training samples to construct a circulant data matrix, which implicitly increases the training samples and reduces both storage and computational complexity.The periodic assumption also introduces unwanted boundary effects. Recently, Spatially Regularized Correlation Filters (SRDCF) solved this issue by introducing penalization on correlation filter coefficients depending on their spatial location. However, SRDCF's efficiency dramatically decreased due to the breaking of circulant structure. We propose Faster Spatially Regularized Discriminative Correlation Filters (FSRDCF) for tracking. The FSRDCF is constructed from Ridge Regression, the circulant structure of training samples in the spatial domain is fully used, more importantly, we further exploit the circulant structure of regularization function in the Fourier domain, which allows our problem to be solved more directly and efficiently. Experiments are conducted on three benchmark datasets: OTB-2013, OTB-2015 and VOT2016. Our approach achieves equivalent performance to the baseline tracker SRDCF on all three datasets. On OTB-2013 and OTB-2015 datasets, our approach obtains a more than twice faster running speed and a more than third times shorter start-up time than the SRDCF. For state-of-the-art comparison, our approach demonstrates superior performance compared to other non-spatial-regularization trackers.