Human Pose Estimation via Depth Map Segmentation
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Solution Overview
Problem
Current systems for tracking and processing human gestures or poses in computing environments face challenges in accurately interpreting commands due to the lack of effective body pose estimation, especially when users are at varying angles to the capture device, leading to potential misinterpretation of gestures and failure to detect smaller movements.
Innovation Solution
The techniques involve processing depth map images from depth cameras to calculate probabilities of pixel association with body parts, constructing models from nodes or joints, and using three-dimensional representations to estimate body poses, including multiple users and background segmentation, to accurately interpret and track user movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If body pose estimation is not performed, then the system is simpler to operate, but gesture interpretation accuracy deteriorates
Solution Approach 1:
The body is segmented into multiple body parts (head, torso, limbs, etc.) with each part having its own pose estimation. This allows the system to process complex gestures by analyzing individual body segments independently, improving gesture interpretation accuracy while managing computational complexity through modular processing.
Solution Approach 2:
The system transitions from two-dimensional image processing to three-dimensional pose estimation by calculating spatial coordinates and orientations of body parts. This dimensional transformation enables accurate gesture recognition regardless of user angle relative to the capture device, resolving the accuracy-complexity contradiction through enhanced spatial understanding.
2Measurement precision
If the capture device does not account for user angle, then the system is easier to implement, but gesture detection accuracy deteriorates
Solution Approach 1:
The system dynamically adjusts pose estimation based on the detected angle of the user relative to the capture device. By making the estimation process adaptive rather than static, the system maintains high gesture detection accuracy across varying user positions without requiring complex pre-programmed angle-specific handling.
Solution Approach 2:
The system uses feedback from angle detection to refine pose estimation. By continuously monitoring user orientation and adjusting the estimation algorithm accordingly, the system compensates for angle variations in real-time, improving detection accuracy without implementing complex angle-dependent processing pipelines.
3Measurement precision
If fine-grained pose estimation is not used, then processing is faster and simpler, but small gesture detection capability deteriorates
Solution Approach 1:
The system segments the body into fine-grained parts including fingers, hands, arms, torso, and head, allowing detection of small gestures such as finger movements. This segmentation enables the system to focus computational resources on relevant body parts, maintaining processing speed while achieving fine-grained detection capability.
Solution Approach 2:
The system applies different levels of processing detail to different body parts based on their relevance for gesture detection. Fine-grained estimation is applied to limb and finger regions where small movements are critical, while coarser processing is applied to the torso and head, optimizing processing speed without sacrificing small gesture detection capability where needed.
Data Source
AI summary
Techniques for human body pose estimation are disclosed herein. Images such as depth images, silhouette images, or volumetric images may be generated and pixels or voxels of the images may be identified. The techniques may process the pixels or voxels to determine a probability that each pixel or voxel is associated with a segment of a body captured in the image or to determine a three-dimensional representation for each pixel or voxel that is associated with a location on a canonical body. These probabilities or three-dimensional representations may then be utilized along with the images to construct a posed model of the body captured in the image.