2D Camera Motion Capture via Virtual 3D Projection
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Solution Overview
Problem
Existing human motion capture systems rely on three-dimensional (3D) sensors, which are not universally available, limiting their use to clinical settings and preventing real-time or near real-time biomechanical assessments outside these environments, and often require complex setups with multiple cameras.
Innovation Solution
A method and system using a single two-dimensional (2D) image sensor to capture and analyze 2D image frames, projecting the output into three dimensions for tri-planar body joint and motion tracking, enabling real-time or near real-time tracking with lower processing power and minimal computational delay.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D sensors are used for human motion capture, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses a 2D camera to capture images and creates a virtual 3D representation by projecting the 2D image onto a 3D coordinate system. This copying approach allows the system to achieve 3D measurement capabilities using a simple 2D sensor, avoiding the need for complex 3D sensors while maintaining measurement precision through computational geometry and marker-based tracking
Solution Approach 2:
The patent replaces the mechanical/physical 3D sensing hardware with a computational approach. Instead of using 3D sensors that physically capture depth information, the system uses 2D image capture combined with mathematical projections and coordinate transformations to derive 3D joint positions, substituting physical sensing complexity with computational algorithms
2Measurement precision
If multiple cameras are used for motion capture, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent makes a single 2D camera perform multiple functions: it captures 2D images, provides reference frames for calibration, enables real-time tracking, and supports both static and dynamic motion analysis. This universal approach eliminates the need for multiple specialized cameras while maintaining comprehensive measurement capabilities
Solution Approach 2:
The patent transforms the measurement problem from a spatial dimension (using multiple cameras in different positions) to a computational dimension (using a single camera and mathematical projections). By adding the dimension of computational processing, the system achieves the same measurement precision as multiple cameras without the physical complexity
3Measurement precision
If 3D sensors are used for real-time tracking, then measurement precision is improved, but processing power requirements increase
Solution Approach 1:
The patent extracts only the essential 2D image information needed for 3D reconstruction, separating the complex computational tasks from the simple data capture. By taking out the 2D image processing step and using it to drive the 3D projection, the system reduces processing power requirements compared to using 3D sensors that would require complex real-time depth calculation and fusion algorithms
4Ease of operation
If 2D image sensors are used for motion capture, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent introduces a virtual 3D coordinate system as an intermediary between the 2D camera and the 3D joint position measurements. This intermediary projection layer translates simple 2D image coordinates into accurate 3D spatial information through mathematical transformations, allowing the system to maintain both ease of operation with a single camera and measurement precision through computational geometry
Data Source
AI summary
Embodiments herein generally relate to methods and systems for human motion capture. In at least one embodiment, there is disclosed a method comprising: accessing one or more input 2D image frames of a subject performing a physical activity, wherein for each input 2D image frame, automatically: detecting one or more image portions corresponding to the subject's joints; identifying one or more pre-defined anchors in the image frame; processing the input 2D image frame to generate a modified input 2D image frame by: overlaying a bounding box and one or more reference axis lines over the input 2D image frame to generate the modified 2D image frame; resolving values for the one or more joint-specific angles generated by the intersection of the one or more reference axis lines; applying a joint coupling model to the determine relative joint positions, of the subject's joints, in a transverse and sagittal planes.