Autonomous UAV Game Imaging Using Rule-Based Action Prediction
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Solution Overview
Problem
Current unmanned autonomous vehicles (UAVs) are limited in capturing images of team sports and activities involving multiple people, as they cannot intelligently follow participants or consider the context of the event they are recording.
Innovation Solution
The UAV determines game play rules, locates game objects, predicts game actions, and calculates optimal image capture positions based on these rules and object locations, allowing it to autonomously capture images of games by moving to strategic positions or selecting nearby UAVs to do so.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current UAVs are used for image capture, then they can follow a single person, but they cannot intelligently follow participants in team sports or activities involving multiple people
Solution Approach 1:
The system segments the playing area into multiple zones and tracks multiple game objects simultaneously. The UAV divides its attention and imaging capability across several participants rather than focusing on a single target, enabling it to follow multiple players in team sports effectively.
Solution Approach 2:
The UAV is equipped with multi-functionality to handle different game types and scenarios. It can switch between tracking individual players, following the ball, and capturing overall game context, making it adaptable to various team sports and activities involving multiple participants.
2Extent of automation
If current UAVs are used for image capture, then they can record video, but they are unable to consider the context of the sport or event that the UAV is recording
Solution Approach 1:
The system incorporates feedback mechanisms where the UAV continuously receives information about game state, player positions, and event context. This feedback loop enables the UAV to understand and adapt to the specific sport being played, adjusting its imaging and tracking behavior accordingly.
Solution Approach 2:
The UAV performs preliminary actions by pre-learning and storing rules of different sports and games. Before capturing footage, it identifies the sport type and loads relevant context information, enabling it to anticipate and understand game dynamics, player movements, and significant events in advance.
3Measurement precision
If the UAV calculates optimal capture positions based on game rules and object locations, then image quality improves, but computational requirements and processing time increase
Solution Approach 1:
The UAV applies partial action by calculating optimal positions only for critical capture moments rather than continuously computing for every frame. It focuses computational resources on key events like goal attempts, fouls, or when the ball enters the scoring zone, reducing overall processing complexity while maintaining high image quality when needed.
Solution Approach 2:
The system dynamically changes computational parameters based on game context. During high-intensity moments, it increases calculation frequency and precision for optimal positioning. During less critical periods, it reduces computational intensity, balancing image quality with processing requirements and preventing system overload.
Data Source
AI summary
Embodiments include devices and methods for capturing images of a game by an unmanned autonomous vehicle (UAV). A processor of the UAV may determine game play rules of the game. The processor may predict a game action based on the determined game play rules. The processor may determine a position from which to capture an image of the game based on the predicted game action, and may move the UAV to the determined position to enable the UAV to capture of an image of the game from the determined position.