CASE STUDIES
Football Sequence Ball Video Annotation
A Leading AI Company
SuperAnnotate
Objective
To improve AI model accuracy for tracking footballs in video sequences, focusing on maintaining temporal consistency, handling occlusions, fast movements, and adjusting for environmental factors.
Process
- Used SuperAnnotate to perform polygon-based annotations on video sequences.
- Reviewed sequences at slower speeds to accurately capture fast movements and occlusions.
- Focused on attributes like ball blur and background to ensure high precision.
- Implemented a robust QA process to maintain temporal consistency across frames.
Challenges
- Occlusions and Fast Movements: Accurately annotating quickly moving objects.
- Ball Differentiation: Identifying active vs. inactive balls.
- Environmental Factors: Adjusting for visibility affected by environmental conditions.
Outcome
Resulted in a 15% increase in AI model accuracy for ball tracking, delivering more reliable insights and predictions for live football broadcasts.
