Unified Probabilistic Model for Human Pose Detection
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Solution Overview
Problem
Conventional methods for human detection and pose estimation in images require separate models and are inefficient, especially when needing to detect humans and classify their pose from a single image, as they fail to account for macroscopic variations and are computationally costly.
Innovation Solution
A probabilistic model is derived using histograms of gradient orientations to simultaneously perform human detection and pose classification, allowing for efficient detection and classification from a single model, either unsupervised or semi-supervised, which clusters images and provides a low-dimensional representation for essential pose information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate models are used for human detection and pose estimation, then each problem can be addressed with specialized techniques, but the computational cost increases and the process becomes inefficient
Solution Approach 1:
The patent combines human detection and pose estimation into a single unified model that performs both functions simultaneously. The model uses a shared feature extraction backbone followed by separate classification heads for detection and pose estimation, eliminating the need for separate processing pipelines and reducing computational overhead while maintaining accuracy.
Solution Approach 2:
The unified model serves multiple functions: it detects human presence, localizes humans in the image, and estimates pose parameters all within a single network architecture. This multi-functional approach allows the system to perform detection and pose estimation efficiently without requiring separate specialized models for each task.
2Ease of operation
If conventional detection methods are used, then human locations can be identified, but macroscopic pose variations are not accounted for
Solution Approach 1:
The patent extends the detection framework by adding pose estimation capabilities to the conventional detection pipeline. While conventional methods operate in the detection dimension (presence/absence), the unified model adds a pose dimension by estimating joint locations and body configuration, enabling the system to handle macroscopic pose variations without complicating the basic detection operation.
3Measurement precision
If multiple separate processing steps are used for detection and pose classification, then each step can be optimized independently, but the overall computational cost becomes significant
Solution Approach 1:
The patent merges the computational resources for detection and pose classification into a single unified processing pipeline. By sharing the feature extraction backbone and using joint training objectives, the model achieves both detection and pose classification accuracy while reducing the total computational cost compared to running separate optimized models independently.
Data Source
AI summary
A method and system efficiently and accurately detects humans in a test image and classifies their pose. In a training stage, a probabilistic model is derived in an unsupervised or semi-supervised manner such that at least some poses are not manually labeled. The model provides two sets of model parameters to describe the statistics of images containing humans and images of background scenes. In a testing stage, the probabilistic model is used to determine if a human is present in the image, and classify the human's pose based on the poses in the training images. A solution is efficiently provided to both human detection and pose classification by using the same probabilistic model to solve the problems.