3D Motion Capture Using Disparity-Based Feature Point Depth Calculation
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Solution Overview
Problem
Current 3D motion capture techniques face challenges in accurately detecting feature points of objects, particularly in varying lighting conditions and non-standard poses, which affects the quality of motion rendering in applications like computer animation and interactive video games.
Innovation Solution
A computing device system that includes image processing modules for extracting foreground images, detecting feature points, calculating depth, and matching motions using disparity images and a graphical user interface to drive virtual characters, enabling accurate 3D motion capture by normalizing pixel grayscale levels, filtering noise, and segmenting images into sections to identify key features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 3D motion capture techniques are used, then motion tracking is achieved, but accuracy in detecting feature points deteriorates under varying lighting conditions and non-standard poses
Solution Approach 1:
The patent segments the image processing into distinct modules: foreground extraction module separates the object from background, feature point detection module identifies key points, and depth calculation module computes 3D positions. This segmentation allows each module to be optimized independently, improving feature point detection accuracy while maintaining adaptability to various conditions.
Solution Approach 2:
The patent transitions from 2D image analysis to 3D motion capture by calculating depth information for each feature point. By adding the depth dimension, the system achieves accurate 3D position detection that is robust to lighting variations and pose changes, as depth information provides additional geometric constraints for feature point identification.
2Measurement precision
If multiple processing modules are added to improve motion detection accuracy, then feature point identification improves, but system complexity increases
Solution Approach 1:
The patent designs processing modules that serve multiple functions: the foreground extraction module not only separates the object from background but also prepares data for feature point detection; the feature point detection module identifies keypoints and provides information for depth calculation. This multi-functionality reduces overall system complexity while maintaining high motion detection accuracy.
Solution Approach 2:
Each processing module is designed to be self-contained and self-sufficient, with internal optimizations that reduce dependency on other modules. For example, the feature point detection module uses algorithms that work directly on the extracted foreground data without requiring additional preprocessing, reducing the need for complex inter-module coordination and simplifying the overall system architecture.
Data Source
AI summary
A computing device in a system for motion detection comprises an image processing device to determine a motion of an object of interest, and a graphical user interface (GUI) module to drive a virtual role based on the motion determined by the image processing device. The image processing device comprises a foreground extracting module to extract a foreground image from each of a first image of the object of interest taken by a first camera and a second image of the object of interest taken by a second camera, a feature point detecting module to detect feature points in the foreground image, a depth calculating module to calculate the depth of each of the feature points based on disparity images associated with the each feature point, the depth calculating module and the feature point detecting module identifying a three-dimensional (3D) position of each of the feature points, and a motion matching module to identify vectors associated with the 3D positions of the feature points and determine a motion of the object of interest based on the vectors.