Planar Object Tracking Based on Fast Corner Shift

A planar object tracking approach based on fast corner shift is proposed to solve the problem that region-based planar tracking approaches are easy to fall into local optimum. Firstly, a pyramid containing images and sets of sample points in the object region is constructed at different scales. Secondly, the fast corner shift approach is implemented at different scales to obtain tracking results, in which a global similarity metric function is used to obtain optimal corner offset iteratively. The experimental results on the planar object tracking dataset POT show that the average tracking accuracy of this approach can reach 49.65% for different scenarios, which is 31.87 percentage points higher than other region-based approaches tested on average. The average tracking time is 0.12 s per frame.