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LEARNING A ROTATION CORRELATION FILTER FOR TRACKING


Abstract


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.

KeyWords
object,tracking,correlation,filter,rotation,estimation,log-polar,transform.



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