Single Object Tracking in the Presence of DistractorsVidenović, Jovana (Avtor) Kristan, Matej (Mentor) Lukežič, Alan (Komentor) computer visionsingle object trackingtracking-by-detectiondata associationdistractorsThis thesis presents a novel approach for single object tracking in the presence of distractors. We define distractors as objects that are visually similar to the tracked target. This visual similarity in practice increases the tracking uncertainty, often leading to tracking failure when the tracker shifts from a target to a distractor. The proposed technique, named Multiple Hypotheses Single Object Tracker (MHSOT), builds upon a recent TransT tracker by including modules for motion estimation, feature extraction, and data association. Additionally, we gather a collection of sequences containing distractors to form the comprehensive Distractor dataset. We evaluate our method, along with the TransT, on the Distractor dataset and its short-term and long-term segments. MHSOT tracker achieves 51.7% mIoU on the whole Distractor dataset, surpassing the TransT by 3.6%. MHSOT and TransT perform better on short-term sequences than on long-term sequences. On short-term sequences, MHSOT outperforms the TransT by 6.1%, reaching 71.5% mIoU. On long-term sequences, MHSOT outperforms the TransT by 3.3% and achieves 50.1% mIoU. MHSOT demonstrates particular superiority over the reference method in re-detecting the target even after extended periods of absence and handling scenarios involving occlusion.20232023-12-04 16:36:33Diplomsko delo/naloga152728sl