A Gesture Recognition Method Based on Head Lightweight Mask Scoring R-CNN

A gesture recognition and lightweight technology, applied in the fields of computer vision and deep learning, can solve the problems of inability to predict gesture masks, insufficient accuracy of gesture detectors, slow gesture detectors, etc., and achieve rich semantic information and lightweight structure. , the effect of reducing the amount of calculation

Active Publication Date: 2022-06-24
DONGHUA UNIV
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is: the precision of the gesture detector in the first stage is not high enough to make a detailed prediction on the gesture mask; the speed of the gesture detector in the second stage is too slow

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
  • A Gesture Recognition Method Based on Head Lightweight Mask Scoring R-CNN
  • A Gesture Recognition Method Based on Head Lightweight Mask Scoring R-CNN
  • A Gesture Recognition Method Based on Head Lightweight Mask Scoring R-CNN

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0043] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these examples are only used to illustrate the present invention and not to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

[0044] like figure 1 Shown is the flow chart of model execution detection. The input image first goes through DetNet59-FPN to extract the multi-scale feature map, and performs 1x1 convolution to obtain a lightweight position-sensitive score map. The RPN network generates anchor boxes and judges the foreground and background and judges the offset. The results are combined with multi-scale feature maps to form RoI input PSRoI Ali...

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 relates to a gesture recognition method based on Head lightweight Mask Scoring R-CNN, which introduces a lightweight position-sensitive score map and position-sensitive RoIAlign after the output feature map of the original Mask Scoring R-CNN backbone network, so that the Head structure The number of input RoI channels becomes very small, and the two consecutive fully connected layers in the Head structure are changed to a single fully connected layer to reduce the amount of calculation. The present invention uses DetNet59 combined with FPN as the backbone network, so that the extracted multi-scale feature map can contain rich semantic information and position information at the same time, and can adapt to objects of various sizes for detection. The average precision of the improved instance segmentation model of the invention is significantly improved, the number of model parameters is effectively reduced, and the training and detection speed of the model are effectively improved.

Description

technical field [0001] The invention relates to a gesture recognition method based on Head lightweight Mask Scoring R-CNN, belonging to the fields of computer vision and deep learning. Background technique [0002] Gesture recognition is an important branch in the field of computer vision. Its core is to use 'machine eyes' to replace human eyes to recognize hand gestures in images or video capture devices, and input the captured images or videos into visual algorithms for calculation. Finally got the hand information. There are many kinds of vision algorithms mentioned here, such as traditional image processing methods and deep learning methods in recent years. Before the advent of deep learning, traditional image processing and machine learning methods could not perform a simple image classification task well, but the emergence of deep learning has made it possible for computers to reach human level. In fact, the emergence of AlphaGo has proved that in some fields, comput...

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
Patent Type & Authority Patents(China)
IPC IPC(8): G06V40/10G06V10/26G06V10/774G06V10/82G06K9/62G06N3/04
CPCG06V40/117G06V40/113G06V10/267G06N3/045G06F18/214
Inventor 徐好好单志勇徐超
Owner DONGHUA UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap