3D Gesture Interaction Analysis With Stereo Pose Estimation
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Solution Overview
Problem
Existing systems face challenges in accurately detecting human-device interactions, particularly in occlusion scenarios and varying lighting conditions, and often fail to provide precise 3D pose estimation and gesture recognition, limiting their versatility and adaptability.
Innovation Solution
A system integrating a detection module, estimation module, and gesture-recognition module uses synchronized stereo imaging and depth-sensing cameras or LiDAR sensors to enhance 3D pose estimation and gesture recognition, providing real-time feedback and customizable gesture recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single-input-image systems are used for pose estimation, then device complexity is reduced, but measurement precision of 3D pose estimation deteriorates
Solution Approach 1:
The patent transitions from 2D single-image input to multi-dimensional stereo imaging input, using multiple camera views to capture spatial information. This dimensional enhancement enables accurate 3D pose estimation by providing depth information through parallax between stereo images, resolving the limitation of 2D-to-3D estimation.
Solution Approach 2:
The patent combines multiple input sources (stereo images and depth maps) into a unified pose estimation framework. By merging visual data from multiple cameras with depth information, the system achieves robust 3D pose estimation that overcomes the limitations of single-image systems while maintaining integrated processing.
2Measurement precision
If specialized hardware is used for interaction detection, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent employs a multi-functional system where a single integrated model performs multiple tasks: hand detection, 3D pose estimation, and gesture recognition. This universal approach eliminates the need for separate specialized hardware for each function, achieving high measurement precision through software intelligence rather than hardware complexity.
Solution Approach 2:
The patent introduces depth maps as an intermediary representation that bridges stereo images and 3D pose estimation. This intermediate depth information facilitates accurate interaction detection without requiring complex specialized hardware, as the depth maps serve as a computational mediator that simplifies the sensing-estimation pipeline.
3Device complexity
If 2.5D pose estimation is provided without true depth information, then device complexity is reduced, but measurement precision of hand joint locations deteriorates
Solution Approach 1:
The patent explicitly adds the true depth dimension by utilizing stereo imaging geometry and depth maps. Instead of estimating relative depths in 2.5D, the system computes true 3D coordinates (X, Y, Z) for hand joints by triangulating from stereo image pairs and integrating depth map information, achieving millimeter-level accuracy in hand joint location.
4Productivity
If real-time analysis is implemented, then productivity improves, but measurement precision may deteriorate due to processing speed constraints
Solution Approach 1:
The patent performs preliminary processing by generating depth maps from stereo images before pose estimation. This pre-computed depth information is then reused during real-time gesture recognition, enabling fast inference without sacrificing accuracy. The preliminary depth extraction acts as a computational shortcut that maintains precision while enabling real-time performance.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves accurate and versatile real-time interaction analysis, enabling immersive user experiences with precise 3D pose perception and adaptable gesture recognition, suitable for interactive applications.
Implementation Method 1
a light detection and ranging (LiDAR) sensor may be used instead of, or in addition to, a stereo camera. The depth-sensing camera and/or the LiDAR sensor may provide depth data directly to the system
Data Source
AI summary
A method and device are disclosed for interaction analysis. The method includes receiving, by an interaction-analysis circuit of a device, input image data from an interaction sensor, generating, by the interaction-analysis circuit, a cropped-and-resized image based on detecting a three-dimensional (3D) object in the input image data, generating, by the interaction-analysis circuit, a 3D pose-estimation structure based on the cropped-and-resized image, determining, by the interaction-analysis circuit, a gesture classification based on the 3D pose-estimation structure, and transmitting the gesture classification.