Robust and fast visual tracking via spatial kernel phase correlation filter

Abstract

In this paper, we present a novel robust and fast object tracker called spatial kernel phase correlation based Tracker (SPC). Compared with classical correlation tracking which occupies all spectrums (including both phase spectrum and magnitude spectrum) in frequency domain, our SPC tracker only adopts the phase spectrum by implementing using phase correlation filter to estimate the object׳s translation. Thanks to circulant structure and kernel trick, we can implement dense sampling in order to train a high-quality phase correlation filter. Meanwhile, SPC learns the object׳s spatial context model by using new spatial response distribution, achieving superior performance. Given all these elaborate configurations, SPC is more robust to noise and cluster, and achieves more competitive performance in visual tracking. The framework of SPC can be briefly summarized as: firstly, phase correlation filter is well trained with all subwindows and is convoluted with a new image patch; then, the object׳s translation is calculated by maximizing spatial response; finally, to adapt to changing object, phase correlation filter is updated by reliable image patches. Tracking performance is evaluated by Peak-to-Sidelobe Ratio (PSR), aiming to resolve drifting problem by adaptive model updating. Owing to Fast Fourier Transform (FFT), the proposed tracker can track the object at about 50frames/s. Numerical experiments demonstrate the proposed algorithm performs favorably against several state-of-the-art trackers in speed, accuracy and robustness.