Prediction-Tracking-Segmentation

Understand

We introduce a prediction driven method for visual tracking and segmentation in videos.

• Instead of solely relying on matching with appearance cues for tracking, we build a predictive model which guides finding more accurate tracking regions efficiently.
• With the proposed prediction mechanism, we improve the model robustness against distractions and occlusions during tracking.
• We demonstrate significant improvements over state-of-the-art methods not only on visual tracking tasks (VOT 2016 and VOT 2018) but also on video segmentation datasets (DAVIS 2016 and DAVIS 2017).