Vehicle Hand Pose Identification via Neural Network Segmentation
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Solution Overview
Problem
Existing hand pose identification techniques in vehicles are computationally expensive and prone to errors due to reliance on bounding box detection and multiple finger joint detection, leading to reduced performance and accuracy, especially when implemented on embedded platforms with power and heat constraints.
Innovation Solution
A method that extracts a hand image from a vehicle image, processes multiple contextual images using neural networks to obtain contextual features, and identifies hand poses using a classifier model, allowing for accurate hand pose recognition with minimal power consumption and heat generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pipelined structure with cascaded modules implementing hand box technique is used, then hand pose identification can be performed, but computational expense increases and errors accumulate in cascaded modules
Solution Approach 1:
The patent extracts and processes only the hand region of interest from the full image, rather than processing the entire image through multiple cascaded modules. This is achieved by detecting hand boundaries and extracting the hand region, then processing only this extracted region for pose identification, thereby reducing computational complexity while maintaining accuracy
Solution Approach 2:
The patent segments the hand into multiple finger regions (thumb, index finger, middle finger, ring finger, little finger) and processes each segment independently. This segmentation approach simplifies the overall processing by breaking down the complex hand pose problem into manageable finger-level segments, reducing computational burden compared to full hand box cascaded processing
2Measurement precision
If multiple finger joints are detected to identify hand pose, then detailed hand pose information is obtained, but computational expense increases
Solution Approach 1:
Instead of detecting all finger joints in the hand, the patent extracts key boundary points of each finger (such as tip, base, and intermediate points) and uses these extracted points to determine finger orientation and pose. This extraction approach provides sufficient pose information without the computational burden of detecting every joint
Solution Approach 2:
The patent uses a partial set of key points on each finger rather than complete joint detection. By identifying only the essential boundary points needed for pose determination (such as finger tip and base points), the system achieves adequate measurement precision with reduced computational complexity compared to full joint detection
3Reliability
If conventional hand pose detection is implemented on embedded platforms, then hand pose recognition is achieved, but power consumption and heat generation increase
Solution Approach 1:
The patent extracts and processes only the necessary hand region and key finger points rather than processing the entire image or all possible features. This selective extraction approach significantly reduces the computational workload on embedded platforms, thereby lowering power consumption and heat generation while maintaining recognition accuracy
Solution Approach 2:
By segmenting the hand into individual fingers and processing each finger independently with simplified models, the patent reduces the overall computational complexity. This segmentation enables efficient processing on resource-constrained embedded platforms, reducing energy usage compared to conventional full-hand processing approaches
Data Source
AI summary
A method for identifying a hand pose in a vehicle involves identifying a hand image for a hand in the vehicle by extraction from a vehicle image of the vehicle. A plurality of contextual images of the hand image is obtained based on the single point. Each of the plurality of contextual images are processed using one or more layers of a neural network to obtain a plurality of contextual features associated with the hand image. A hand pose associated with the hand is identified based on the plurality of contextual features using a classifier model.