Articulated Body Pose Estimation Using 3D Depth Data
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Solution Overview
Problem
Current techniques face challenges in accurately detecting, tracking, and estimating the pose of articulated bodies, such as hands, due to viewpoint variability, complex articulations, and self-occlusions, especially when relying on 2D image data, and there is a need for improved methods using 3D data from consumer-grade sensors.
Innovation Solution
The implementation of an iterative closest point (ICP) technique combined with inverse kinematics (IK) for refining kinematic model parameters, allowing for accurate alignment of a kinematic model with 3D point clouds from consumer-grade sensors, using methods like particle swarm optimization, Levenberg Marquardt, and iterative closest point inverse kinematics (ICPIK) to handle non-rigid transformations and kinematic constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If 2D image data is used for detecting and tracking articulated bodies, then the device complexity is reduced, but the measurement precision and reliability deteriorate due to viewpoint variability, complex articulations, and self-occlusions
Solution Approach 1:
The patent transitions from 2D image data to 3D depth data for articulated body detection and tracking. By using depth information from consumer-grade 3D sensors, the system overcomes the limitations of 2D data including viewpoint variability, complex articulations, and self-occlusions, thereby improving measurement precision while maintaining device complexity at an acceptable level through the use of affordable sensors and efficient algorithms.
2Manufacturing precision
If conventional optimization techniques are used for matching articulated body models to 3D data, then the manufacturing precision is improved, but the productivity deteriorates due to high computational time
Solution Approach 1:
The patent applies preliminary action by performing model-to-data matching in an optimized sequence and using pre-computed kinematic constraints to guide the optimization process. This approach reduces the search space for optimization algorithms, enabling faster convergence to accurate pose estimates without sacrificing precision, thereby improving computational speed while maintaining pose estimation accuracy.
3Measurement precision
If iterative optimization methods are applied to refine kinematic model parameters, then the measurement precision is improved, but the loss of time increases due to multiple iterations required for convergence
Solution Approach 1:
The patent implements feedback mechanisms where the optimization process continuously evaluates the match between the articulated body model and 3D depth data, using the error signals to guide parameter refinement. This feedback-driven approach ensures that iterative optimization converges efficiently to accurate pose estimates, minimizing the number of iterations required while maintaining high measurement precision through intelligent use of depth information and kinematic constraints.
Data Source
AI summary
Techniques related to pose estimation for an articulated body are discussed. Such techniques may include extracting, segmenting, classifying, and labeling blobs, generating initial kinematic parameters that provide spatial relationships of elements of a kinematic model representing an articulated body, and refining the kinematic parameters to provide a pose estimation for the articulated body.