Real-Time Rigid Body Motion Prediction Using Marker Proximity
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Solution Overview
Problem
Traditional motion capture systems fail to track rigid bodies in real-time when their markers become occluded by objects or environmental noise, leading to difficulties in recovering the pose of the rigid body and ambiguity in differentiating between multiple rigid bodies.
Innovation Solution
A computer-implemented method predicts the position of an untracked rigid body in a current frame using previously tracked data and identifies unlabeled markers proximate to the predicted position, reconstructing the rigid body based on these markers, even when fewer than three markers are visible.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional motion capture systems track markers in real-time, then tracking precision is maintained, but the system fails when markers become occluded by objects or environmental noise
Solution Approach 1:
The system performs preliminary action by predicting the future position of the rigid body using previously tracked data before the occlusion completely disrupts tracking. This prediction is used to proactively search for and identify unlabeled markers in the anticipated location, allowing the system to maintain continuous tracking even when markers are temporarily occluded.
2Duration of action of stationary object
If the system reconstructs rigid body pose using previously tracked data, then tracking continuity is maintained during occlusion, but data storage requirements increase
Solution Approach 1:
The system extracts only the essential elements needed for prediction and reconstruction - specifically the position and orientation data from previously tracked frames. Rather than storing complete rigid body data, the system extracts minimal necessary information (position, orientation) to perform prediction and marker identification, reducing storage requirements while maintaining tracking continuity.
3Measurement precision
If the system uses prediction to locate unlabeled markers, then reconstruction accuracy improves, but computational complexity increases
Solution Approach 1:
The system applies local quality by concentrating computational resources only in the predicted region of interest rather than processing the entire scene. The prediction algorithm identifies a specific location where unlabeled markers are likely to be found, and the marker identification process is focused exclusively on this localized area, improving accuracy while reducing overall computational complexity.
Data Source
AI summary
Methods and systems for real-time rigid body motion prediction are provided. The method includes determining an occurrence of an untracked rigid body in a current frame, the untracked rigid body being tracked in a previous frame. The method also includes predicting a position in the current frame of the untracked rigid body using previously tracked data in the previous frame. The method also includes identifying unlabeled markers in the current frame that are proximate to the predicted position of the untracked rigid body. The method also includes reconstructing the untracked rigid body in the current frame based on the identifying of the unlabeled markers.