Automated Video Analysis Workflow with AI Highlight Generation

Automated Video Analysis and Highlight Generation

1. Video Acquisition

1.1 Source Identification

Identify sources for video data, including game footage, training sessions, and player highlights.

1.2 Video Upload

Utilize cloud storage solutions such as Google Drive or AWS S3 for secure video uploads.

2. Pre-Processing

2.1 Video Formatting

Ensure videos are in a compatible format (e.g., MP4, AVI) for analysis.

2.2 Frame Extraction

Implement tools like OpenCV to extract frames from video at predetermined intervals.

3. AI-Driven Analysis

3.1 Player Detection

Utilize AI models such as YOLO (You Only Look Once) for real-time player detection within the video frames.

3.2 Action Recognition

Employ deep learning frameworks like TensorFlow or PyTorch with pre-trained models to identify specific actions (e.g., goals, assists, tackles).

3.3 Performance Metrics Extraction

Integrate tools like StatsBomb or Hudl for comprehensive metrics analysis, including player positioning and movement efficiency.

4. Highlight Generation

4.1 Highlight Compilation

Aggregate identified key actions into a highlight reel using video editing software such as Adobe Premiere Pro or an automated solution like Magisto.

4.2 AI-Enhanced Editing

Leverage AI tools like Wibbitz for automated video editing, ensuring smooth transitions and optimal highlight selection.

5. Review and Quality Assurance

5.1 Manual Review

Conduct a quality check by scouting professionals to validate AI-generated highlights and performance metrics.

5.2 Feedback Loop

Incorporate feedback into the AI models to enhance accuracy and performance in future analyses.

6. Delivery and Integration

6.1 Highlight Distribution

Share highlights with scouts and coaches through platforms like Hudl or via secure links to cloud storage.

6.2 Data Integration

Integrate analyzed data into scouting databases or CRM systems using APIs for seamless access and reporting.

7. Continuous Improvement

7.1 Model Retraining

Regularly update AI models with new data to improve accuracy and adapt to evolving gameplay styles.

7.2 Performance Analysis

Analyze user engagement and feedback to refine the workflow and enhance user experience.