Hand Pose Estimation Using Dynamic Rigidity Detection
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Solution Overview
Problem
Accurately estimating hand poses from video is computationally expensive due to the complex dynamics and structural dependencies of hand joints, leading to a tradeoff between accuracy, speed, and computational complexity.
Innovation Solution
The system detects whether a hand has remained rigid between frames, allowing for efficient estimation of rigid hand poses by calculating translation and rotation, reducing computational costs by selecting between expensive and inexpensive pose estimation computations based on hand rigidity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If accurate hand pose estimation is performed using complex computations, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system dynamically adjusts the pose estimation computation based on detected hand rigidity. When hand rigidity is detected, a simplified computation model is used; when rigidity is not detected, a more complex computation model is applied. This dynamic adaptation resolves the contradiction by matching computational complexity to the actual complexity of the hand pose being estimated.
Solution Approach 2:
The system changes the parameter of computation complexity based on the detected hand rigidity state. By detecting whether the hand is rigid or articulated, the system selects appropriate computation parameters (simplified vs. complex models), thereby achieving accurate pose estimation while avoiding unnecessary computational complexity.
2Measurement precision
If accurate hand pose estimation is performed using complex computations, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The system dynamically selects between simplified and complex pose estimation computations based on real-time detection of hand rigidity. This dynamic selection enables the system to process rigid hand poses quickly using simplified models while maintaining accuracy for articulated poses using complex models, thereby improving overall processing productivity without sacrificing measurement precision.
Solution Approach 2:
The computation parameter is changed based on the detected hand state. When hand rigidity is detected, the system switches to a faster, simplified computation mode; when articulation is detected, it uses a more accurate but slower computation mode. This parameter adaptation resolves the contradiction by optimizing processing speed for common rigid poses while maintaining accuracy for articulated poses.
3Productivity
If rigid hand pose estimation is used, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The system dynamically determines hand rigidity between frames and adjusts the pose estimation approach accordingly. When rigidity is detected, efficient rigid pose estimation is applied; when articulation is detected, full articulated pose estimation is used. This dynamic adaptation ensures that productivity is improved for rigid poses while measurement precision is maintained for articulated poses.
Solution Approach 2:
The system changes the estimation model parameter based on detected hand rigidity. By detecting whether the hand maintains a rigid configuration, the system selects the appropriate estimation parameter (rigid model for efficiency, articulated model for precision), thereby resolving the contradiction between productivity and measurement precision.
Data Source
AI summary
The disclosed computer-implemented method may include (1) receiving a present frame of a video stream, the present frame comprising a present depiction of a multi-segment articulated body system, (2) identifying a previous frame of the video stream that comprises a previous depiction of the multi-segment articulated body system, (3) analyzing the present frame and the previous frame to determine whether the multi-segment articulated body system remained substantially rigid between the previous frame and the present frame, and (4) estimating a pose of the multi-segment articulated body system in the present frame using a first pose estimation computation that treats the multi-segment articulated body system as rigid and that is selected in contrast to a second pose estimation computation based on determining that the multi-segment articulated body system remained substantially rigid between the previous frame and the present frame. Various other methods, systems, and computer-readable media are also disclosed.