  In object tracking, outlier is one of primary factors which degrade
performance of image-based tracking algorithms. In this respect, therefore,
most of the existing methods simply discard detected outliers and pay little or
no attention to employing them as an important source of information for motion
estimation. We consider outliers as important as inliers for object tracking
and propose a motion estimation algorithm based on concurrent tracking of
inliers and outliers. Our tracker makes use of pyramidal implementation of the
Lucas-Kanade tracker to estimate motion flows of inliers and outliers and final
target motion is estimated robustly based on both of these information.
Experimental results from challenging benchmark video sequences confirm
enhanced tracking performance, showing highly stable target tracking under
severe occlusion compared with state-of-the-art algorithms. The proposed
algorithm runs at more than 100 frames per second even without using a hardware
accelerator, which makes the proposed method more practical and portable.
