Golf Tracking System Sensor Fusion for Automated Scoring
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Solution Overview
Problem
Existing systems for monitoring and managing data from sensors around a golf course struggle to efficiently track golf play with minimal human interaction, particularly in accurately associating shot locations with player strokes for scoring and statistical purposes.
Innovation Solution
A method and system that utilize a network of sensors, including radar, camera, and laser devices, to automatically capture and associate coordinate sets with hole events, generating detailed scoring data by filtering and matching sensor data with minimal human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated sensor systems are deployed to track golf play, then productivity and efficiency improve, but device complexity increases
Solution Approach 1:
The system divides the golf course into multiple zones with specific sensor placements (radar guns at tees and greens, cameras at strategic locations). Each sensor type handles specific tracking functions, and data is processed in discrete stages: coordinate capture, event detection, association matching. This segmentation manages complexity while maintaining high automated scoring productivity.
Solution Approach 2:
The system employs multi-functional sensor devices that can detect multiple parameters simultaneously. Radar devices track ball flight coordinates and calculate trajectory data. Camera systems capture both player positions and ball locations. This multi-functionality reduces the total number of devices needed while improving scoring data generation efficiency.
2Measurement precision
If multiple sensor types are used to capture comprehensive tracking data, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system merges data from multiple sensor types (radar, camera, laser) into a unified coordinate system. Radar provides precise ball flight trajectory data, cameras capture player and ball positions, and laser devices measure distances. These diverse measurements are integrated and cross-validated to achieve high shot location accuracy while managing device complexity through centralized processing.
Solution Approach 2:
The system introduces intermediate processing layers including coordinate transformation algorithms, trajectory prediction models, and automated association engines. These intermediaries translate raw sensor data from different types into standardized scoring information, enabling high measurement precision while abstracting away the complexity of multiple sensor types from the final scoring output.
3Loss of time
If automated association algorithms are implemented to match hole events with coordinate sets, then loss of time is reduced, but device complexity increases
Solution Approach 1:
The system performs preliminary data preparation by pre-processing sensor coordinates into standardized formats, pre-categorizing hole events by type and location, and pre-establishing association rules based on spatial and temporal parameters. This preliminary organization enables rapid automated matching during actual play, reducing data processing time while managing complexity through structured preparation.
Solution Approach 2:
The automated association system operates autonomously without requiring manual intervention. The algorithm automatically matches hole events with corresponding coordinate sets by comparing timestamps, locations, and event types. This self-service capability eliminates time-consuming manual data entry and processing while the modular algorithm structure keeps system complexity manageable.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and efficient tracking of golf play, allowing for automated generation of scoring data and minimal human interaction, thereby improving the efficiency and accuracy of golf event monitoring and management.
Implementation Method 1
The plurality of sensor device coordinate sources may include at least a radar device, and wherein the plurality of coordinate sets includes coordinate sets corresponding to predicted shot to locations generated by a prediction generator using trajectory data of the corresponding golf ball.
Implementation Method 2
The plurality of sensor device coordinate sources may include one or more radar devices, one or more camera devices, and one or more laser devices.
Implementation Method 3
The plurality of sensor device coordinate sources may include one or more radar devices, one or more camera devices, and one or more laser devices.
Data Source
AI summary
A method of tracking golf play includes receiving a plurality of coordinate sets corresponding to a plurality of shot to locations of golf balls hit by a plurality of players on a hole in a golf event. The plurality of coordinate sets were automatically captured in sensor data of a plurality of sensor device coordinate sources positioned around the hole. Hole events corresponding to strokes of the plurality of players on the hole in the golf event are received that were automatically detected in sensor data of a sensor device hole event sources. Each hole event may be automatically associated with one of the plurality of coordinate sets that correspond to the shot to location of the golf ball for the stroke the hole event corresponds to generate detailed scoring data for the plurality of players on the hole in the golf event.