Human Pose Estimation Using Tree-Based Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current human pose estimation methods in unconstrained videos are complex, often result in sub-optimal solutions due to their NP-hard nature, and struggle with modeling symmetric body parts effectively, especially with the need for expensive depth sensors and limited applicability with standard video cameras.
Innovation Solution
A method using a standard video camera that employs tree-based optimization to estimate human poses by introducing abstract body part nodes to combine symmetric parts, eliminating the need for depth sensors and simplifying the problem formulation, thereby reducing costs and increasing applicability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video-based human pose estimation methods are used, then temporal consistency and accuracy are improved, but computational complexity increases to NP-hard
Solution Approach 1:
The patent segments the pose estimation problem into two distinct stages: (1) generating candidate pose hypotheses for each frame independently, and (2) selecting the optimal sequence of poses across frames using dynamic programming. This segmentation transforms the NP-hard joint optimization problem into manageable sub-problems that can be solved efficiently.
Solution Approach 2:
The patent extracts and separates the temporal consistency constraint from the pose estimation process. Instead of jointly optimizing spatial accuracy and temporal consistency, the method first generates spatially accurate candidate poses for each frame, then extracts the temporal dimension as a separate optimization layer using dynamic programming to select the best pose sequence.
2Loss of information
If symmetric body parts are modeled separately in tree structure, then detailed pose information is captured, but double counting issues occur causing sub-optimal solutions
Solution Approach 1:
The patent merges symmetric body parts (left and right ankles, knees, elbows, shoulders) into unified abstract nodes in the graphical model. This merging prevents double counting by treating symmetric parts as a single entity with combined constraints, while still capturing their individual positions and relationships through the hypothesis generation and selection process.
3Measurement precision
If depth sensors are used for pose estimation, then measurement accuracy is improved, but system cost increases significantly
Solution Approach 1:
The patent creates a virtual 3D representation (copy) of the scene from 2D video frames by generating multiple pose hypotheses that represent possible 3D configurations. This virtual modeling approach achieves depth information without physical depth sensors, using computational geometry and constraint satisfaction instead of expensive hardware like Kinect's infrared projector and monochrome CMOS sensor.
4Ease of manufacture
If standard video cameras are used, then system cost is reduced, but applicability to unconstrained video is limited
Solution Approach 1:
The patent develops a universal pose estimation framework that works with standard video cameras across diverse unconstrained scenarios. The method is designed to handle varying lighting conditions, background clutter, different viewpoints, and various human poses without requiring specialized equipment, making it broadly applicable to real-world video surveillance and human-computer interaction applications.
Data Source
AI summary
A system and method for estimating a sequence of human poses in an unconstrained video. In the present invention, a unified two stage, tree-based, optimization problem is solved for which an efficient and exact solution exists. While the proposed method finds an exact solution, it does not sacrifice the ability to model the spatial and temporal constraints between body parts in the video frames on the unconstrained video.