Segmented Pose Estimation Networks for Resource-Limited Devices
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Solution Overview
Problem
Existing human pose estimation algorithms are not suitable for devices with limited computing capacity, leading to low accuracy and inability to support applications effectively.
Innovation Solution
A lightweight neural network structure is developed by adjusting the block and bottleneck units of the feature extraction and backend prediction neural networks, integrating them to reduce computational load while maintaining high accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing human pose estimation algorithms are used, then pose estimation capability is provided, but the algorithms cannot be implemented on devices with limited computing capacity
Solution Approach 1:
The neural network is divided into two separate modules: a backbone network for feature extraction and a prediction network for pose estimation. This segmentation allows each module to be optimized independently, reducing the overall computational burden on terminal devices while maintaining pose estimation functionality.
Solution Approach 2:
The patent transforms the traditional single-network architecture into a modular two-network architecture, adding a dimensional structure to the system. This dimensional change enables better resource management and computational efficiency on constrained devices.
2Measurement precision
If existing human pose estimation algorithms are implemented on terminal devices, then pose estimation is performed, but the accuracy is insufficient to meet application requirements
Solution Approach 1:
By separating feature extraction and pose prediction into distinct networks, each can be specialized and optimized for its specific task. The backbone network focuses on extracting meaningful features efficiently, while the prediction network specializes in accurate pose estimation, achieving both speed and accuracy.
Solution Approach 2:
The backbone network serves multiple purposes: it extracts features for pose estimation and can be reused across different pose estimation scenarios. This multi-functionality improves computational efficiency without compromising accuracy.
3Measurement precision
If high-performance pose estimation algorithms are used, then accurate pose estimation is achieved, but the algorithms cannot be supported by terminal devices with limited computing capacity
Solution Approach 1:
The divided architecture allows the backbone network to be computationally efficient for mobile devices while the prediction network maintains high accuracy requirements. This segmentation enables accurate pose estimation with reduced energy consumption on terminal devices.
Data Source
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AI summary
Provided is a method for pose estimation in a device, the method comprising capturing an image; estimating poses of an object included in the captured image; obtaining skeleton information of the object based on the estimating of the poses of the object; and processing the skeleton information of the object for at least one of detecting blocking of the object, detecting the poses of the object and adjusting content based on detected virtual object distinct from human body poses.