Resilient Projection Mapping with Markerless Object Tracking
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Solution Overview
Problem
Existing projection mapping systems are limited in their ability to accurately track and render 3D content onto various physical objects in real-time, requiring resource-intensive processing and manual configuration, which restricts scalability and the types of objects that can be tracked.
Innovation Solution
A projection mapping system that utilizes image-capturing devices to segment image data, perform contour detection, and dynamically select optimal projection devices for rendering 3D content onto physical objects without the need for dot markers, thereby reducing processing load and enhancing scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If marker-based methods are used for tracking objects, then object tracking accuracy is improved, but processing resources and set-up time increase significantly
Solution Approach 1:
The patent extracts and removes the marker-based tracking mechanism from the system, replacing it with markerless tracking using image-capturing devices. This eliminates the need for physical markers on objects while maintaining tracking capability through computational methods that analyze image data to identify and track objects without artificial markers.
Solution Approach 2:
The patent replaces the mechanical marker-based tracking system with an optical-computational system using image-capturing devices and processing units. Instead of relying on physical markers and mechanical recognition, the system uses image processing algorithms to detect, track, and identify objects in real-time.
2Measurement precision
If high fidelity and high frame rate image capturing devices are used, then object tracking accuracy is improved, but device cost and complexity increase
Solution Approach 1:
The patent segments the image processing task into multiple stages: initial image capture, contour detection, tracking over time, and validation. This segmentation allows the system to process images from standard cameras efficiently by breaking down the complex tracking problem into manageable computational steps rather than requiring a single complex high-performance camera system.
Solution Approach 2:
The system uses the image data itself to generate the tracking and identification functions. By processing the captured images through contour detection and temporal tracking algorithms, the system creates its own tracking capability without requiring specialized high-fidelity capturing hardware, allowing standard devices to serve the tracking function through computational enhancement.
3Measurement precision
If manual configuration of object pose and location is performed, then tracking accuracy is improved, but system setup time and operational complexity increase
Solution Approach 1:
The system performs self-configuration by automatically detecting object contours, calculating pose and location through image processing, and initiating tracking without manual intervention. The computational system analyzes image data autonomously to determine object parameters, eliminating the need for operators to manually configure tracking parameters or mark objects.
Solution Approach 2:
The system performs preliminary contour detection and object identification automatically before tracking begins. By pre-processing images to extract contours and characteristics, the system prepares tracking data autonomously, eliminating the need for manual setup and configuration steps while maintaining accurate pose and location determination.
4Measurement precision
If resource-intensive processing is performed for real-time tracking and rendering, then tracking accuracy is improved, but system scalability is limited
Solution Approach 1:
The patent segments the processing workload into efficient computational steps: image capture, contour detection, temporal tracking, and rendering. This segmentation allows the system to handle multiple objects simultaneously by processing them through standardized algorithms, improving scalability while maintaining accuracy through optimized computational rather than resource-intensive processing.
Solution Approach 2:
The system adjusts processing parameters dynamically based on scene complexity and object characteristics. By modifying tracking sensitivity, contour detection thresholds, and rendering resolution according to actual conditions, the system maintains high tracking accuracy across diverse scenarios without requiring proportionally increased computational resources, thereby improving scalability.
Data Source
AI summary
Systems and methods for dynamically tracking objects, and projecting rendered 3D content onto said objects in real-time. The methods described herein further include image data capture performed by various image-capturing devices, wherein said data is segmented into various components to identify one or more projectors for rendering and projecting 3D content components onto one or more objects.