AR Object Placement on Finger via ML and CV Segmentation
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Solution Overview
Problem
Traditional augmented-reality systems face challenges in accurately synchronizing digital constructs with real-world environments due to computational complexity, leading to lag and disconnects, especially when dealing with three-dimensional rendering and real-time adjustments to user movements and body positions.
Innovation Solution
The system uses a camera to capture images of a user's hand, trains a machine learning model to identify key points, and combines this with computer vision to track hand gestures and movements, allowing for precise placement and orientation of augmented-reality objects, reducing the need for wearable trackers and improving real-time processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional augmented-reality systems use complex computational methods for three-dimensional rendering and real-time adjustments, then the accuracy of synchronizing digital constructs with real-world environments is improved, but the processing speed and responsiveness deteriorate due to computational complexity
Solution Approach 1:
The system segments the hand-tracking problem into two distinct stages: (1) coarse localization using machine learning to identify key points and determine gesture, and (2) fine localization using computer vision to detect edge contours and extrapolate precise locations. This segmentation allows each method to operate at its optimal level, with ML providing robust initial estimates and CV refining the precision, thereby achieving high synchronization accuracy without sacrificing processing speed.
Solution Approach 2:
The machine learning model performs preliminary action by first identifying key points and determining the gesture of the user's hand before the computer vision method is applied. This preliminary localization narrows down the search space and provides initial estimates that guide the subsequent fine localization process, enabling the system to achieve precise tracking results more efficiently than if computer vision were applied to the entire hand region without prior guidance.
2Measurement precision
If traditional systems rely on wearable trackers for precise tracking, then the tracking accuracy is improved, but the device complexity and user burden increase
Solution Approach 1:
The system applies self-service by using the user's own hand as the tracking target without requiring any external wearable devices. The machine learning and computer vision algorithms process images of the user's hand directly, extracting key points, gestures, and contours to enable precise tracking. This eliminates the need for additional trackers or sensors, reducing device complexity while maintaining high tracking accuracy through algorithmic analysis of the hand's visual features.
3Reliability
If the system processes the entire hand image for tracking, then the robustness is improved, but the processing time and computational resources increase
Solution Approach 1:
The system segments the processing task by first identifying a targeted area containing the finger portion relevant to the augmented-reality object placement. The computer vision method then focuses exclusively on this targeted area to detect edge contours, rather than processing the entire hand image. This segmentation maintains tracking robustness by concentrating computational resources on the critical region while significantly reducing overall processing time and resource consumption.
Solution Approach 2:
The system applies local quality by directing the computationally intensive computer vision edge contour detection only to the targeted area that contains the finger portion, rather than applying it uniformly across the entire hand image. This localized processing approach maintains high tracking robustness in the critical region while optimizing processing efficiency by avoiding unnecessary computation in other areas of the hand.
Data Source
AI summary
A computer-implemented method for displaying augmented-reality objects may include capturing, by an augmented-reality system, an image of a user's hand. The method may also include applying a machine learning model to the image to identify a set of key points and determine a gesture of the hand based on the key points. In addition, the method may include identifying a targeted area of the image containing a portion of a finger based on the key points and the gesture and, subsequently, detecting a pair of edge contours of the finger by performing a computer vision method on the targeted area. Furthermore, the method may include extrapolating a location to place an augmented-reality object on the finger. Finally, the method may include displaying, by the augmented-reality system, the augmented-reality object at the location on the finger by tracking the key points. Various other methods, systems, and computer-readable media are also disclosed.