Research on Robust Tracking Algorithm for Planar Object Against Occlusion and Blur
This paper proposes a rendering-based data genera-tion method that can simulate motion blur,defocus blur and other effects caused by camera movement,and builds a new data set based on this to support the research of planar target tracking algorithms. In addition,this new planar object track-ing model based on image optical flow and planar mask multi-task learning. Specifically,the method eliminates background and occlusion interference through the estimated plane mask,and screens out reliable dense optical flow estimates to im-prove the robustness of the tracking algorithm. The applica-tion of the planar target tracking algorithm on public datasets and supplementary data reveals that its accuracy and robust-ness improve,and it can be effectively tracked under condi-tions such as blur,occlusion,and lack of texture.
planar object trackingoptical flow estimationmask estimationdataset construction
NETL
NSTL
万方数据
