Learning Dual-Domain Calibration and Distance-Driven Correlation Filter: A Probabilistic Perspective for UAV Tracking

Abstract

Unmanned Aerial Vehicle (UAV) tracking has been possible because of the growth of intelligent information technology in smart cities, making it simple to gather data at any time by dynamically monitoring events, people, the environment, and other aspects in the city. The traditional filter creates a model to address the boundary effect and time filter degradation issues in UAV tracking operations. But these methods ignore the loss of data integrity terms since they are overly dependent on numerous explicit previous regularization terms. In light of the aforementioned issues, this work suggests a dual-domain Jensen-Shannon divergence correlation filter (DJSCF) model address the probability-based distance measuring issue in the event of filter degradation. The two-domain weighting matrix and JS divergence constraint are combined to lessen the impact of sample imbalance and distortion. Two new tracking models that are based on the perspectives of the actual probability filter distribution and observation probability filter distribution are proposed to translate the statistical distance in the online tracking model into response fitting. The model is roughly transformed into a linear equality constraint issue in the iterative solution, which is then solved by the alternate direction multiplier method (ADMM). The usefulness and superiority of the suggested strategy have been shown by a vast number of experimental findings.