Robust scale and rotation estimation is an important and challenging problem in visual object tracking. There have been proposed many sophisticated trackers to track the location of a target accurately, but most of them do not take much attention to the scale and rotation estimation. Inspired by the success of the correlation filters in visual tracking, we proposed a novel scale-and-rotation correlation filter (SRCF) in the Fourier domain to realize the scale and rotation estimation. We thus constructed a tracker with this scale-and-rotation correlation filter and a corrected kernel correlation filter. Our tracker was tested on a full benchmark dataset consisting of 50 video in comparison with fifteen state-of-art trackers. Both the success plots and the precision plots show that our tracker achieved superior performance in real time.
['Yan Li', 'Guizhong Liu']
Learning a scale-and-rotation correlation filter for robust visual tracking