LAUSR

Abstract This paper presents a novel correlation filter-based tracking method for robust visual object tracking in the presence of partial occlusion, large-scale variation and model drift. To do this, first, we develop a correlation filter for predicting the target location based on the distribution of correlation response. In this formulation, the correlation response of the target image follows Gaussian distribution to estimate the target location efficiently. Second, the constraints are derived using kernel ridge regression to mitigate the target failure in object tracking. Third, we propose an adaptive scale estimation method to detect the target scale changes during the tracking. In addition, two feature integration is elaborately designed to improve the discriminative strength of the correlation filter. Finally, extensive experimental results on OTB2013, OTB2015, TempleColor128 and UAV123 datasets demonstrate that the proposed method performs favourably against several state-of-the-art methods.