Real-time visual tracking via robust Kernelized Correlation Filter

There has been an increasing interest in the use of correlation filters for visual object tracking due to their impressive tracking performance. However, existing correlation filter based tracking methods, such as Struck and Kernelized Correlation Filter (KCF), cannot always solve tracking problems in complicated conditions such as heavy occlusion and aggressive motion. In this paper, we proposed a real-time visual tracker via a robust KCF. We start by implementing a search window alignment, based on a motion model with uncertainty, which increases the tracking accuracy for fast moving targets and reduces the padding value to accelerate tracking speed. Next, we establish a combined confidence measurement including occlusion information, which is utilized for robust updating. Then we apply an adaptive Kalman filter to improve the tracking accuracy. Qualitative and quantitative experimental results show that the proposed algorithm outperforms the state-of-the-art methods such as KCF and Struck.