Autonomous AI Camera System for Golf Video Production
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Solution Overview
Problem
Current digital media production in sports and video recording is labor-intensive and expensive, requiring human intervention for capturing and processing video footage, which lacks an automatic means to process and communicate video efficiently without a director or videographer.
Innovation Solution
An autonomous digital media processing system comprising AI-enabled cameras and network media processing services that detect and record golfers at golf holes, validate user profiles, and automatically activate video recording, processing, and communication, using AI logic to identify objects and gestures, and transmit processed videos to mobile applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If human directors and videographers are used to capture and process video footage, then video quality and production control are improved, but labor costs and production time increase significantly
Solution Approach 1:
The system enables autonomous video capture and processing where AI cameras automatically detect golfers, track their movements, capture footage, and transmit videos without requiring human directors or videographers. The RMS and NMS components work together to self-manage the entire video production workflow.
Solution Approach 2:
The patent replaces the mechanical human-operated video capture system with an automated AI-based system. AI cameras with golfer detection algorithms substitute for human videographers, and automated processing replaces manual video production workflows, thereby improving efficiency while maintaining quality.
2Loss of information
If multiple cameras and human operators are deployed to capture sports events, then comprehensive footage coverage is improved, but system complexity and operational costs increase
Solution Approach 1:
The AI-enabled cameras are designed to perform multiple functions: detecting golfers, tracking movements, capturing footage from fixed positions, and transmitting videos. This multi-functional approach replaces the need for multiple specialized cameras and operators, reducing system complexity while maintaining comprehensive coverage.
Solution Approach 2:
The Remote Media System (RMS) and Network Media Processing Services (NMS) act as intermediaries that coordinate between multiple AI cameras, manage footage collection, and handle processing workflows. This centralized intermediary approach simplifies the complexity of managing multiple cameras by providing a unified control and processing layer.
3Speed
If real-time video streaming is implemented for sports events, then viewer engagement is improved, but network infrastructure requirements and transmission costs increase
Solution Approach 1:
The system captures and processes video footage in real-time as golfers move through the course, preparing videos for transmission before they are needed. This preliminary action allows efficient batch processing and reduces the energy burden on network infrastructure during peak viewing times.
Solution Approach 2:
The AI cameras periodically capture footage at key moments (e.g., when golfers approach, swing, or complete holes) rather than continuously streaming. This periodic capture approach reduces data transmission requirements and network energy consumption while maintaining viewer engagement with key moments.
Data Source
AI summary
A system for monitoring and recording and processing an activity includes one or more cameras for automatically recording video of the activity. A remote media system is located at the location of the activity. A network media processor and services is communicatively coupled with the remote media system. The remote media system includes one or more AI enabled cameras. The AI enabled camera is configured to record the activity. The network media processor is configured to receive an activation request of the AI enabled camera and validate the record request.