Core Concepts
Improved method TAPIR+ for tracking static points in videos.
Abstract
1. Abstract
Proposes TAPIR+ for Tracking Any Point (TAP) task.
Addresses cumulative error in point tracking.
Utilizes Multi-granularity Camera Motion Detection.
Achieved top rank in final test with a score of 0.46.
2. Introduction
Deep learning techniques in single-point tracking.
Zero-shot strategy with OmniMotion, TAPIR, and Cotraker.
TAPIR used as the baseline due to better performance.
3. Method
TAPIR employs two-stage approach for point trajectory prediction.
TAPIR+ focuses on rectifying tracking of static points in static camera videos.
Multi-granularity Camera Motion Detection to distinguish camera shots.
CMR-based point trajectory prediction for moving and static camera videos.
4. Experiment
Relies on TAPIR's pre-trained model for zero-shot approach.
Evaluation metric: Average Jaccard (AJ).
TAPIR+ outperforms other methods in static camera shots.
Ablation study shows the contribution of each component in TAPIR+.
5. Conclusion
Summary of the solution for Point Tracking Task in ICCV 1st Perception Test Challenge 2023.
Based on camera moving detection and moving object identification.
Stats
Our approach ranked first in the final test with a score of 0.46.
TAPIR+ outperforms other TAP methods by achieving about 2.79 performance improvements.