AR Tracking Point Supplementation for Stability
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Solution Overview
Problem
Augmented reality (AR) tracking methods face instability and lag due to the loss of tracking points over time, leading to frame skipping and increased computing resources required for re-recognition, which affects the stability and continuity of the AR experience.
Innovation Solution
An image processing method that supplements tracking points when their number falls below a preset threshold, using a set of matching points in the current frame as initial tracking points and determining supplementary points within a polygon to maintain stable tracking, combined with Kalman filtering for posture transformation optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If tracking is performed using only initial matching points without supplementation, then the device complexity is reduced, but the reliability of tracking deteriorates due to loss of tracking points over time
Solution Approach 1:
The system automatically supplements tracking points when their数量 falls below the threshold, enabling self-maintenance of tracking stability without external intervention. The processor monitors tracking point quantity and autonomously initiates supplementation operations.
Solution Approach 2:
The system dynamically adjusts the quantity of tracking points by supplementing new points when the tracking point quantity parameter falls below the preset threshold, thereby maintaining optimal tracking performance through parameter control.
2Reliability
If re-recognition is performed frequently to recover lost tracking points, then the tracking reliability is improved, but the productivity deteriorates due to increased computing resource consumption
Solution Approach 1:
The system performs preliminary supplementation of tracking points within the current frame before proceeding to the next frame, preventing tracking failure rather than recovering from it. This proactive approach avoids the need for costly re-recognition operations.
Solution Approach 2:
The system supplements only the necessary number of tracking points to reach the preset threshold, rather than performing full re-recognition of the entire scene. This partial action approach maintains tracking continuity with minimal computational overhead.
3Stability of the object's composition
If the number of tracking points is maintained above the threshold through supplementation, then the stability of AR tracking is improved, but the loss of time increases due to additional point determination and addition operations
Solution Approach 1:
The system supplements tracking points locally within the target picture region rather than searching the entire frame. This localized approach maintains tracking point set stability while minimizing the time required for point determination and addition.
Solution Approach 2:
The system continuously monitors tracking point quantity and performs supplementation only when necessary to maintain the threshold, ensuring continuous tracking stability without unnecessary operations that would waste time.
Data Source
AI summary
An image processing method and an image processing includes, when a current frame contains a target picture, taking the current frame as a reference frame for tracking and taking a set of matching points in the current frame, which match the target picture as an initial set of tracking points, to perform tracking of the target picture; obtaining a next frame and determining a set of tracking points of the next frame based on the initial set of tracking points; determining whether the number of tracking points in the set of tracking points is less than a first preset threshold; and, when the number of tracking points in the set of tracking points is less than the first preset threshold, determining supplementary tracking points and adding the supplementary tracking points to the set of tracking points.