Concurrent Tracking of Inliers and Outliers
This work addresses performance issues in object tracking for computer vision applications, offering a practical and portable solution with incremental improvements over existing methods.
The paper tackles the problem of object tracking degradation due to outliers by proposing a motion estimation algorithm that concurrently tracks both inliers and outliers, resulting in enhanced tracking performance with highly stable tracking under severe occlusion and a speed of over 100 frames per second.
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.