AI Driven Video Analysis and Highlight Generation Workflow

Video Analysis and Highlight Generation

1. Data Collection

1.1 Video Acquisition

Collect game footage from various sources including camera feeds, drones, and streaming services.

1.2 Data Annotation

Utilize annotation tools to label key events in the footage such as goals, fouls, and player movements.

2. Video Processing

2.1 Pre-Processing

Apply video stabilization and enhancement techniques to improve quality using tools like OpenCV.

2.2 AI Integration

Implement AI algorithms for object detection and tracking using frameworks like TensorFlow or PyTorch.

3. Analysis Phase

3.1 Event Detection

Use machine learning models to automatically identify significant events, leveraging tools like Sportscode or Hudl.

3.2 Performance Metrics

Analyze player performance metrics through AI-driven analytics platforms such as Catapult or Stats Perform.

4. Highlight Generation

4.1 Automated Highlight Creation

Generate highlight reels by compiling detected events using AI tools like Wyscout or Veo.

4.2 Manual Review

Allow coaches and analysts to review and edit generated highlights for accuracy and relevance.

5. Distribution

5.1 Internal Sharing

Share highlights with coaching staff and players through dedicated platforms such as Coach’s Eye or TeamSnap.

5.2 External Sharing

Disseminate highlights on social media and team websites using automated posting tools like Hootsuite or Buffer.

6. Feedback Loop

6.1 Data Analysis

Collect feedback from coaches and players on the usefulness of highlights for performance improvement.

6.2 Continuous Improvement

Refine AI models and processes based on feedback to enhance future video analysis outcomes.