Asynchronous Camera Tracking via Embedded Time Codes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for tracking moving objects or persons in live events face challenges with precise synchronization of cameras, leading to cumbersome and error-prone 3D rendition quality, and require extensive equipment and labor for genlocking, with potential signal delays and reflections causing synchronization failures.
Innovation Solution
An automated system that generates four-dimensional biomechanical models using cameras that are not necessarily identical or precisely synchronized, allowing asynchronous operation with a shared time synchronizing code, and stores data in a queriable database for correlation analysis to improve sports performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cameras are precisely synchronized using genlocking, then synchronization accuracy is improved, but device complexity and labor requirements increase significantly
Solution Approach 1:
The patent removes the genlocking synchronization requirement entirely from the system. Each camera operates independently with its own internal clock, extracting the complex synchronization infrastructure and replacing it with asynchronous operation and post-processing time correlation using shared time codes embedded in video streams.
Solution Approach 2:
The patent introduces shared time codes as an intermediary mechanism. Instead of directly synchronizing camera clocks through genlocking, time codes serve as a common reference that allows asynchronous cameras to be correlated in post-processing, mediating between independent camera operations and synchronized analysis.
2Measurement precision
If genlocking is used for camera synchronization, then timing precision is improved, but signal delays and reflections cause synchronization failures
Solution Approach 1:
The patent extracts the vulnerable genlocking synchronization signal path from the system. By eliminating the need for distributed clock signals and their physical transmission paths, the system removes the source of signal delays and reflections that cause synchronization failures.
Solution Approach 2:
Each camera serves itself by using its own internal clock for capture timing. The system becomes self-synchronized through independent operation rather than relying on external clock distribution, making each camera autonomous and eliminating synchronization vulnerabilities.
3Measurement precision
If multiple identical cameras are used for tracking, then measurement accuracy is improved, but equipment costs and setup complexity increase
Solution Approach 1:
The patent changes the parameter of camera identity from 'identical' to 'different'. The system accepts cameras with different specifications, positions, and operational characteristics, using software-based parameter normalization and time correlation to achieve accurate multi-object tracking without requiring identical hardware.
4Ease of operation
If asynchronous camera operation is allowed, then ease of operation is improved, but data correlation difficulty increases
Solution Approach 1:
Shared time codes embedded in video streams serve as an intermediary that facilitates data correlation between asynchronous cameras. The time codes provide a common temporal reference that enables automated alignment and correlation of data from different camera sources without manual synchronization.
Data Source
AI summary
A plurality of tracking cameras is pointed towards a routine hovering area of an in-the-field sports participant who routinely hovers about that area. Spots within the hovering area are registered relative to a predetermined multi-dimensional coordinates reference frame (e.g., Xw, Yw, Zw, Tw) such that two-dimensional coordinates of 2D images captured by the tracking cameras can be converted to multi-dimensional coordinates of the reference frame. A body part recognizing unit recognizes 2D locations of a specific body part in the 2D captured images and a mapping unit maps them into the multi-dimensional coordinates of the reference frame. A multi-dimensional curve generator then generates a multi-dimensional motion curve describing motion of the body part based on the mapped coordinates (e.g., Xw, Yw, Zw, Tw). The generated multi-dimensional motion curve is used to discover cross correlations between play action motions of the in-the-field sports participant and real-world sports results.