Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Faster R-CNN-based hand posture detection and identifying method

A recognition method and gesture detection technology, applied in character and pattern recognition, instruments, biological neural network models, etc., can solve the problems of poor robustness and low accuracy, and achieve the effect of improving accuracy and enhancing robustness.

Active Publication Date: 2017-10-10
ZHEJIANG UNIV OF TECH
View PDF5 Cites 36 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the shortcomings of poor robustness and low accuracy of existing gesture recognition methods, the present invention provides a gesture detection and recognition method based on Faster R-CNN that enhances robustness and improves accuracy

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Faster R-CNN-based hand posture detection and identifying method
  • Faster R-CNN-based hand posture detection and identifying method
  • Faster R-CNN-based hand posture detection and identifying method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0025] The present invention will be further described below in conjunction with the accompanying drawings.

[0026] refer to Figure 1 to Figure 4 , a Faster R-CNN-based gesture detection and recognition method for gesture detection and recognition. The gesture detection and recognition methods described in the embodiments of the present application mainly refer to using the Faster R-CNN network and the perturbation overlap rate algorithm.

[0027] The overall structure of the network used in the embodiments of this application is as follows figure 1 shown. Input the gesture label data into the Faster R-CNN network, and input the nonlinear features output in the shared convolutional layer to the region extraction network RPN and FastR-CNN network; then feed back the region suggestion of the gesture target obtained by the RPN network to Fast R-CNN network; Finally, the Fast R-CNN network outputs the gesture position and gesture category through the classification layer and ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a faster R-CNN-based hand posture detection and identifying method. The method includes the following steps: S1. using a Faster R-CNN network, setting parameters in a network for a hand posture identifying application; S2. making a marker for a hand posture sample, making the marker sample as input of the Faster R-CNN network, outputting effective non-linear features from a shared convolutional layer and inputting the effective non-linear features to a region proposal network RPN and the Fast R-CNN; the RPN which has the disturbance overlap rate algorithm acquiring a region proposal of a hand posture, and feedbacking the region proposal to the Fast R-CNN; and S3. the Fast R-CNN outputting a hand posture position and a hand posture type through a classification layer and a frame regression layer. The invention also provides a Faster R-CNN-based hand posture detection and identifying method which has increased robustness and higher accuracy.

Description

technical field [0001] The invention relates to a computer-based pattern recognition technology, in particular to a gesture detection and recognition technology based on a convolutional neural network, and in particular to a gesture detection and recognition method based on Faster Region-based Convolutional Neural Networks (referred to as Faster R-CNN). Background technique [0002] Since the 21st century, scholars have proposed many gesture detection and recognition methods, mainly using image segmentation methods to separate gesture images from the background, and then perform template matching to recognize gestures. Early research mainly focused on gesture recognition based on data gloves, but it is inconvenient to use, and the device cost is high, which is not conducive to human-computer interaction in practical environments. The other is a method based on computer vision. The more commonly used methods include Histogram of Oriented Gradient (HOG) feature and Support Vec...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06K9/00G06N3/04
CPCG06V40/113G06V40/28G06N3/045
Inventor 张江鑫吴晓凤徐欣晨
Owner ZHEJIANG UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com