Distractor-aware Siamese Networks for Visual Object Tracking

Understand

Recently, Siamese networks have drawn great attention in visual tracking community because of their balanced accuracy and speed.

• However, features used in most Siamese tracking approaches can only discriminate foreground from the non-semantic backgrounds.
• The semantic backgrounds are always considered as distractors, which hinders the robustness of Siamese trackers.
• In this paper, we focus on learning distractor-aware Siamese networks for accurate and long-term tracking.