Lightweight CNN Pose Analysis via Segmented Feature Extraction
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Solution Overview
Problem
Existing human pose analysis methods are not robust enough to handle natural images and require heavy computations, making them unsuitable for real-time analysis on inexpensive devices like consumer electronics or mobile devices.
Innovation Solution
A system and method using lightweight convolutional neural networks (CNNs) to extract human pose information from images, comprising a feature extractor connected to a database of reference images, and various modules for detecting 2D and 3D body skeletons, body silhouettes, hand silhouettes, and facial keypoints, utilizing deep CNN architectures with lightweight layers for efficient computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural network models are used for robust human pose analysis, then measurement precision and reliability are improved, but device complexity and computational requirements increase
Solution Approach 1:
The system segments the complex deep learning task into multiple specialized detector modules, each responsible for a specific aspect of pose analysis (body skeleton, hand skeleton, facial keypoints, body silhouette). This modular segmentation allows each module to be optimized independently and reduces the computational burden on any single component while maintaining overall system accuracy.
Solution Approach 2:
The system implements a universal feature extractor that serves multiple detector modules simultaneously. This multi-functional component extracts human-related features once and provides them to all downstream detectors (body skeleton detector, hand skeleton detector, facial keypoints detector, etc.), eliminating redundant feature extraction computations and reducing overall system complexity.
2Measurement precision
If heavy computation methods are used for real-time analysis, then measurement precision is improved, but productivity and processing speed decrease
Solution Approach 1:
The system performs preliminary feature extraction in advance, creating a comprehensive set of human-related features from the input image before passing them to multiple specialized detectors. This preliminary action of extracting all necessary features upfront allows downstream detectors to work with pre-processed data, significantly reducing their computational requirements and enabling real-time processing while maintaining high precision.
3Reliability
If robust deep learning methods are deployed, then reliability is improved, but ease of operation and device requirements worsen
Solution Approach 1:
The system divides the robust pose analysis task into segmented detector modules that can be selectively activated based on device capabilities and analysis requirements. This segmentation allows the system to be deployed on inexpensive devices by enabling users to activate only the necessary detectors (e.g., only body skeleton detection on mobile devices) while maintaining robust performance through the specialized architecture of each module.
Solution Approach 2:
The universal feature extractor serves as a multi-functional component that provides standardized features to all detector modules, enabling the system to maintain reliability across different deployment scenarios. This universal component ensures consistent feature quality whether the system is running on high-end or inexpensive devices, facilitating easier deployment across diverse hardware platforms.
Data Source
AI summary
System and method for extracting human pose information from an image, comprising a feature extractor connected to a database, a convolutional neural network (CNN) with a plurality of CNN layers. Said system/method further comprising at least one of the following modules: a 2D body skeleton detector for determining 2D body skeleton information from the human-related image features; a body silhouette detector for determining body silhouette information from the human-related image features; a hand silhouette detector for determining hand silhouette detector from the human-related image features; a hand skeleton detector for determining hand skeleton from the human-related image features; a 3D body skeleton detector for determining 3D body skeleton from the human-related image features; and a facial keypoints detector for determining facial keypoints from the human-related image features.