Prediction-Tracking-Segmentation
This work addresses the problem of accurate and robust video object tracking and segmentation for computer vision applications, representing an incremental advancement over existing methods.
The paper tackles visual tracking and segmentation in videos by introducing a prediction-driven method that improves robustness against distractions and occlusions, achieving significant improvements on VOT 2016, VOT 2018, DAVIS 2016, and DAVIS 2017 datasets.
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).