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 games or events involving multiple participants, as they cannot intelligently follow team sports or children's play and fail to consider the context of the sport or event being recorded.
Innovation Solution
The implementation of methods on a UAV processor to determine game play rules, locate game objects, predict game actions, and calculate optimal image capture positions based on game context, allowing the UAV to capture images of games or events with improved contextual understanding and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current UAVs use simple tracking algorithms to follow a single person, then the UAV can easily capture video of individual subjects, but the UAV cannot intelligently follow multiple participants in team sports or children's play
Solution Approach 1:
The system segments the game area into multiple zones and divides tracking of multiple participants into separate detection tasks for each zone. The processor independently identifies and tracks different participants in different zones, then synthesizes this information to determine optimal capture positions, enabling multi-subject tracking without overwhelming system complexity
Solution Approach 2:
The system transitions from two-dimensional planar tracking to three-dimensional spatial positioning by calculating optimal capture positions in 3D space above the game area. This dimensional expansion allows the UAV to capture multiple participants simultaneously from elevated perspectives while maintaining contextual awareness of the entire game scene
2Loss of information
If the UAV uses fixed capture positions to record events, then the system operation is simple, but the captured footage lacks contextual understanding of the sport or event
Solution Approach 1:
The system performs preliminary identification of game boundaries, landmarks, and participants before capturing footage. By pre-processing the scene to understand the sport context and participant positions, the UAV can then automatically determine optimal capture positions that preserve contextual information without requiring complex real-time decision-making
Solution Approach 2:
The system continuously monitors participant positions, game zone configurations, and captured footage quality, using this feedback to dynamically adjust capture positions. This closed-loop control maintains contextual understanding by adapting to changing game states while keeping system operation relatively simple through automated adjustments
3Measurement precision
If the UAV dynamically adjusts capture positions to follow game action, then the captured footage improves in relevance and accuracy, but the system complexity increases significantly
Solution Approach 1:
The system employs a universal processor that performs multiple functions: identifying game boundaries, detecting landmarks, locating participants, calculating optimal positions, and controlling UAV movement. This multi-functional approach improves footage accuracy through integrated processing while avoiding the complexity of separate specialized systems for each function
Solution Approach 2:
The system implements dynamic capture position adjustment by continuously calculating optimal positions based on real-time participant locations and game zone configurations. The UAV automatically adapts its position and orientation to maintain optimal framing of game action, improving footage accuracy through dynamic responsiveness rather than static pre-positioning
Data Source
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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 determine a location of the game object. The processor may calculate a position from which to capture an image of the game based on the determined game play rules and the location of the game object. The processor may capture an image of the game from the calculated position.