Fast and object-adaptive spatial regularization for correlation filters based tracking.

Spatially-regularized correlation filters have achieved great successes in visual object tracking, with excellent tracking accuracy and robustness to various interferences. Performance improvement is mainly attributed to spatial regularization (SR), which is a powerful tool to alleviate the boundary effects of correlation filters (CF) based tracking. However, SR also causes high complexity, which cannot achieve real-time tracking. Furthermore, the fixed and handcrafted SR map, disregarding object’s appearance during the whole sequence, seems incapable of handling changeable scenes. In this paper, we propose a fast and object-adaptive spatial regularization (FOSR) model to alleviate those drawbacks. By introducing FOSR method, more discriminative filters can be efficiently obtained by jointly learning in spatial and frequency domain. Besides, an object-adaptive SR map that contains object information can be offline and online learned within a data-driven manner. Extensive experiments on two benchmarks, OTB-2015 and VOT-2016, validate the effectiveness and generality of our model in helping state-of-the-art SR based trackers to achieve more than 5 times of speedup and a relative gain of 3.7% and 3.3% in success and precision plots on OTB-2015, respectively. Additionally, FOSR can help pure CF based trackers to remarkably improve their accuracy with comparable speed.