Random Forest Subspace Regression for Pose Correction
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Solution Overview
Problem
Initial pose estimation from depth images using classification-based methods is prone to errors due to pose variation and body part occlusion, necessitating additional processing for robustness in applications like gaming and human-computer interaction.
Innovation Solution
The use of random forest regression methods for pose correction, specifically through subspace learning, to learn and correct systematic errors in initial skeleton estimation, providing a more data-driven and efficient approach compared to nearest neighbor methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classification-based methods are used for initial pose estimation, then the pose can be obtained from depth images, but the estimation is prone to error due to pose variation and body part occlusion
Solution Approach 1:
The patent introduces an intermediate correction step between initial pose estimation and final pose determination. A random forest regression model serves as a mediator that takes the initial estimated pose and depth image data as input, and outputs a corrected pose by learning the mapping from erroneous estimates to ground truth poses. This intermediary processing step resolves the contradiction by filtering out errors caused by occlusion and pose variation while preserving the overall pose structure.
Solution Approach 2:
The patent performs preliminary error analysis and correction model training before actual pose estimation. The random forest regression model is pre-trained on a dataset containing both erroneous initial poses and corresponding ground truth poses. This preliminary action enables the system to compensate for systematic errors in real-time pose estimation without requiring complex processing during the actual measurement, thereby improving both accuracy and robustness.
2Measurement precision
If random forest subspace regression is applied to correct initial skeleton estimation, then systematic errors are reduced, but additional processing time and computational complexity are introduced
Solution Approach 1:
The random forest regression model is pre-trained offline on a comprehensive dataset, which transfers the computational burden of complex regression calculations to the training phase rather than the real-time inference phase. During actual pose estimation, the pre-trained model performs rapid predictions by evaluating trained decision trees, significantly reducing processing time while maintaining high accuracy in correcting systematic errors.
Solution Approach 2:
The patent replaces traditional iterative optimization methods (which are computationally intensive and time-consuming) with a random forest regression model. This substitution uses statistical learning and ensemble methods to directly predict corrected pose parameters, eliminating the need for repeated iterative calculations and substantially reducing processing time while improving accuracy in correcting systematic estimation errors.
Data Source
AI summary
Embodiments are disclosed that relate to the correction of an estimated pose determined from depth image data. One disclosed embodiment provides, on a computing system, a method of obtaining a representation of a pose of articulated object from image data capturing the articulated object. The method comprises receiving the depth image data, obtaining an initial estimated skeleton of the articulated object from the depth image data, applying a random forest subspace regression function to the initial estimated skeleton, and determining the representation of the pose based upon a result of applying the random forest subspace regression to the initial estimated skeleton.