Human Pose Estimation Using Inner Distance Shape Context Descriptors
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Solution Overview
Problem
Current methods for estimating human pose from visual inputs face challenges such as complexity in modeling human motion, kinematic constraints, self-occlusions, varying illumination, and computational intensity, leading to inefficient and inaccurate real-time detection and recognition of human features.
Innovation Solution
The system employs Inner Distance Shape Context (IDSC) descriptors to detect and compare feature points in depth images, augment missing features, and enforce kinematic constraints to estimate human pose, using a pose estimation system with modules for feature detection, interpolation, augmentation, and reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If model-based approaches are used to estimate pose from visual inputs, then the system can utilize known parametric human models and kinematic constraints, but the computational complexity increases and real-time performance deteriorates
Solution Approach 1:
The patent segments the human body into distinct body parts (head, torso, arms, legs) and represents each part with simplified geometric primitives (spheres, cylinders). This segmentation allows the complex pose estimation problem to be broken down into tracking individual body parts, reducing overall computational complexity while maintaining accuracy through the parametric model constraints.
Solution Approach 2:
The patent transforms the pose estimation problem from tracking 3D positions of multiple body points to estimating a small set of parametric variables (position and orientation of body segments). By changing the parameter representation from numerous 3D coordinates to fewer kinematic parameters, the computational burden is reduced while the parametric human model ensures physically plausible poses.
2Adaptability or versatility
If learning-based approaches are used to detect body features, then the system can directly estimate pose from image quantities, but the computational intensity remains high and only pre-programmed poses can be recognized
Solution Approach 1:
The patent performs preliminary segmentation of the human figure silhouette from the depth image and identifies contour points before feature detection. This preliminary processing organizes the data in a structured manner, enabling faster comparison with the parametric model and reducing the computational load during the actual pose estimation phase.
Solution Approach 2:
The patent replaces traditional image-based feature detection with depth image-based detection. By using depth information instead of intensity information, the system achieves more robust and accurate feature detection with reduced computational requirements, as depth data provides direct geometric constraints that simplify the detection process.
3Measurement precision
If traditional feature detection methods are used in depth images, then the system can identify body features, but the detection accuracy decreases under self-occlusions and varying appearances
Solution Approach 1:
The patent transitions from 2D intensity-based image processing to 3D depth-based processing. By utilizing the third dimension (depth), the system can accurately detect body features even when they are self-occluded in the 2D projection or when illumination conditions vary. The depth information provides explicit geometric constraints that are immune to lighting changes and occlusion ambiguities.
Solution Approach 2:
The patent changes the detection parameter from image intensity to depth distance. This parameter change makes the detection process invariant to illumination variations and improves accuracy under self-occlusions, as depth values directly represent geometric relationships regardless of lighting conditions or visible surface area.
4Reliability
If high dimensional configuration spaces are searched deterministically, then the system can enforce kinematic constraints, but the computational time increases significantly
Solution Approach 1:
The patent segments the high-dimensional configuration space into smaller subspaces corresponding to individual body segments. By optimizing each segment independently and then combining results, the system enforces kinematic constraints more efficiently without needing to search the entire high-dimensional space simultaneously, reducing computational time while maintaining constraint enforcement.
Solution Approach 2:
The patent applies partial optimization by focusing computational effort on the most critical constraints and body segments rather than exhaustively optimizing all parameters. This selective approach enforces essential kinematic constraints (joint limits, self-penetration avoidance) while accepting approximate solutions for less critical aspects, achieving reasonable accuracy with reduced computation time.
Data Source
AI summary
A system, method, and computer program product for estimating human body pose are described. According to one aspect, a human figure silhouette is segmented from a depth image of a human actor. Contour points are sampled along the human figure silhouette. Inner Distance Shape Context (IDSC) descriptors of the sample contour points are determined and compared to IDSC descriptors of the feature points in an IDSC gallery for similarity. For each of the feature points, the sample contour point with the IDSC descriptor that is most similar to an IDSC of the feature point is identified as that feature point in the depth image. An estimated pose of a human model is estimated based on the detected feature points and kinematic constraints of the human model.