Synchronized IR Camera Tracking for Multiple Moving Objects
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Solution Overview
Problem
Existing systems struggle to efficiently and automatically track multiple moving objects, such as players and a ball, in real-time without manual interaction, especially in dynamic environments like sporting events.
Innovation Solution
A tracking system utilizing IR cameras synchronized with transponders in helmets and RF transmitters to actively address and track multiple objects, employing spatial and temporal diversity for robust identification, and adaptive triggering for high temporal resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple objects are tracked simultaneously with high temporal resolution, then tracking accuracy and frequency are improved, but system complexity and computational requirements increase
Solution Approach 1:
The system divides the tracking task into separate modules: object detection, tracking, and rendering. Each object is processed independently through detection at multiple temporal resolutions, then merged into a unified tracking representation. This segmentation allows high temporal resolution tracking without proportionally increasing overall system complexity.
Solution Approach 2:
The system dynamically adjusts the temporal resolution of tracking based on object motion characteristics. Objects with high velocity or frequent position changes are tracked at higher temporal resolutions, while stationary or slow-moving objects are tracked at lower resolutions. This dynamic adaptation maintains tracking accuracy where needed while reducing computational load elsewhere.
2Productivity
If adaptive triggering is used to adjust tracking frequency based on object importance or location, then resource efficiency is improved, but measurement consistency across objects may be affected
Solution Approach 1:
The system applies different tracking frequencies and temporal resolutions to different objects based on their local characteristics and importance. Critical objects (players, ball) receive higher tracking frequency while less important objects receive lower frequency. This local quality differentiation maintains measurement consistency for important objects while improving overall resource efficiency.
Solution Approach 2:
The system changes the temporal resolution parameter dynamically based on object properties and event context. When an object is detected as important or in a critical location, the temporal resolution parameter is increased. This parameter adaptation allows consistent measurement for important objects while reducing resources for less critical tracking.
3Reliability
If spatial and temporal diversity are employed for robust identification, then object identification reliability is improved, but data processing requirements increase
Solution Approach 1:
The system performs preliminary object detection and classification before full tracking begins. Objects are pre-identified and categorized by importance and motion characteristics, allowing the system to allocate appropriate tracking resources in advance. This preliminary action ensures reliable identification for important objects while avoiding excessive data processing for less critical objects.
Solution Approach 2:
The system applies partial tracking (lower temporal resolution) to less important objects and excessive tracking (higher temporal resolution) to important objects. This selective application of tracking intensity maintains identification reliability for critical objects while reducing overall data volume by applying full tracking only where necessary.
Data Source
AI summary
A system simultaneously tracks multiple objects. All or a subset of the objects includes a wireless receiver and a transmitter for providing an output. The system includes one or more wireless transmitters that send commands to the wireless receivers of the multiple objects instructing different subsets of the multiple objects to output (via their respective transmitter) at different times. The system also includes object sensors that receive output from the transmitters of the multiple objects and a computer system in communication with the object sensors. The computer system calculates locations of the multiple objects based on the sensed output from the multiple objects.