Abstract
Object tracking is one of the most important and rapidly developing fields in computer vision. On the other hand, 360° videos and related technologies are getting popular these years. In this thesis, we present a view-warping based method to enhance and expand existing online object tracking algorithms to be used on 360° videos. In addition, we propose a new benchmark dataset for evaluating 360° video object tracking algorithms and for analyzing the tracking results.