AR Image Processing with Adaptive Crop-and-Scale Tracking
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Solution Overview
Problem
Existing image processing techniques for AR devices are inefficient and often require, as they do not effectively scale or crop indiscriminately, leading to computational expense and suboptimal results in object tracking.
Innovation Solution
An adaptive image processing technique dynamically determines the crop-and-scale order based on object tracking parameters, such as historical data and device motion, to optimize the region of interest for efficient processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image processing uses fixed crop-and-scale operations, then the processing pipeline is simple, but computational efficiency is poor and object tracking accuracy is suboptimal
Solution Approach 1:
The patent implements dynamic crop-and-scale operations where the cropping region and scaling factors are adaptively adjusted based on real-time object tracking parameters, camera motion, and scene depth information. This allows the system to optimize the region of interest dynamically rather than using fixed operations, thereby improving computational efficiency without requiring overly complex processing pipelines.
Solution Approach 2:
The system changes processing parameters such as crop region coordinates, scale factors, and region of interest definitions based on detected object positions, camera movements, and depth data. By varying these parameters adaptively, the system achieves better object tracking accuracy and computational efficiency compared to static processing approaches.
2Measurement precision
If the system processes entire images indiscriminately, then processing is straightforward, but computational resources are wasted and tracking precision is reduced
Solution Approach 1:
The patent extracts and processes only the relevant region of interest containing the tracked object, rather than processing the entire image. By identifying and isolating the object's location based on tracking parameters and camera motion, the system extracts only the necessary image data for processing, significantly reducing computational resource consumption while maintaining or improving tracking accuracy.
Solution Approach 2:
The system segments the input image into relevant and irrelevant regions based on object tracking information and camera motion analysis. By dividing the processing task into focused regions of interest rather than handling the complete image uniformly, the system reduces unnecessary computations while preserving the precision needed for accurate object tracking.
Data Source
AI summary
Examples describe adaptive image processing for an augmented reality (AR) device. An input image is captured by a camera of the AR device, and a region of interest of the input image is determined. The region of interest is associated with an object that is being tracked using an object tracking system. A crop-and-scale order of an image processing operation directed at the region of interest is determined for the input image. One or more object tracking parameters may be used to determine the crop-and-scale order. The crop-and-scale order is dynamically adjustable between a first order and a second order. An output image is generated from the input image by performing the image processing operation according to the determined crop-and-scale order for the particular input image. The output image can be accessed by the object tracking system to track the object.