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.