Visual Object Tracking Based on Adaptive Background-Awareness and Spatial Constraint

Abstract

The correlation filter method is effective in visual tracking tasks, whereas it suffers from the boundary effect and filter degradation in complex situations, which can result in suboptimal performance. Aiming at the solving above problem, this study proposes an object tracking method with a discriminant correlation filter, which combines an adaptive background perception and a spatial dynamic constraint. In this method, an adaptive background-awareness strategy is used to filter the background information trained by the interference filter to improve the discriminability between the object and the background. In addition, the spatial regularization term is introduced, and the dynamic change of the real filter and the predefined spatial constraint template is used to optimize filter learning to enhance the spatial information capture ability of the filter model. Experiments on the OTB100, VOT2018, and TrackingNet standard datasets demonstrate that our method achieves favorable accuracy and success rates. Compared with the current popular correlation filter methods, the proposed method can still maintain stable tracking performance with a scene scale variation, complex background, motion blur, and fast motion.