Discriminative correlation filter with segmentation and context for robust trackingLampe, Ajda (Avtor) Kristan, Matej (Mentor) computer visionvisual object trackingtracking by detectioncorrelationsegmentationVisual object tracking is an area in the field of computer vision, which has seen great popularity increase due to a large availability of video data. There are many different tracking tasks, such as multiple object tracking, long-term tracking and specialized trackers, expected to perform well in a very specific domain. In this work, we focus on online generic short-term single object tracking, which can be considered the base visual tracking task and can be adaptable to any of the previously mentioned tasks. We propose a new tracker, based on correlation filtering, augmented with context information and a predicted object segmentation mask. The results on benchmarks fall far behind the current state-of-the-art, however the proposed method consistently outperforms baseline trackers, which shows the methods potential for future improvements.20202020-11-12 10:10:03Magistrsko delo/naloga121958VisID: 24961COBISS_ID: 40159235sl