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

Method and system for optimizing human-computer interaction interface of intelligent cabin based on three-branch decision

A technology of human-computer interaction interface and cockpit, which is applied in the direction of user/computer interaction input/output, computer components, mechanical mode conversion, etc. It can solve the problems of low gesture recognition accuracy and slow recognition speed, and reduce interaction time. Comfortable interactive experience, accurate gesture recognition effect

Active Publication Date: 2018-12-28
CHONGQING UNIV OF POSTS & TELECOMM
View PDF3 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Based on the above problems, using the ability of deep neural network to extract features, combined with multi-granularity information expression and three-branch decision-making ideas, choosing the appropriate granularity can simultaneously solve the optimization problem of low accuracy and slow recognition speed of gesture recognition

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
  • Method and system for optimizing human-computer interaction interface of intelligent cabin based on three-branch decision
  • Method and system for optimizing human-computer interaction interface of intelligent cabin based on three-branch decision
  • Method and system for optimizing human-computer interaction interface of intelligent cabin based on three-branch decision

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0044] The present invention comprises the following steps:

[0045] S1. Collect gesture video in the cockpit, preprocess it, and obtain a static gesture image;

[0046] S2. Segmenting the gesture and the background in the gesture image to obtain a gesture area image;

[0047] S3. Perform multi-grained expression for the gesture area image from coarse-grained to fine-grained; use convolutional neural network to extract multi-granularity features of the gesture area image;

[0048] S4. Calculate the conditional probability of classifying the image of each granularity gesture region into each category from coarse-grained to fine-grained, and use the three decision-making steps to sequentially complete gesture recognition;

[0049] S5. Perform semantic conversion on the recognized gesture area image, and operate the human-computer interaction interface according to the gesture recognition result after semantic conversion;

[0050] The gesture region image is expressed in multip...

Embodiment 2

[0077] On the basis of steps S1-S5, this embodiment also adds step S6, which adopts the method of weighted summation to obtain the optimal granularity, and takes the optimal granularity as the finest granularity, and repeatedly executes steps S3-S5.

[0078] HMI interface optimization design method such as Figure 4 As shown, the final human-computer interaction interface optimization result of each granularity is obtained by weighted summation, so as to determine the optimal granularity of the gesture area image, and use the optimal granularity as the finest granularity, and use the convolutional neural network to analyze new gestures. Extract multi-granularity features and make three decisions sequentially;

[0079] Result=w×Acc+(1-w)×Time

[0080] Time=T 1 +T 2

[0081] Among them, Result is the optimal granularity of the gesture area image, Acc indicates the gesture recognition accuracy, Time indicates the time spent in the gesture recognition process, w indicates the ...

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 belongs to the field of intelligent driving, and relates to a method and system for optimizing the human-computer interaction interface of an intelligent cabin based on three-branch decision. The method comprises the following steps: collecting gesture video in the cabin, preprocessing, and obtaining gesture image; segmentation of gesture image and background to obtain gesture regionimage; for multi-granularity expression, the multi-granularity feature of gesture region image being extracted by a convolution neural network; from coarse granularity to fine granularity, the imageclassification of each granularity gesture region to the conditional probability of each kind of gesture region being calculated, and the gesture recognition being accomplished sequentially by using three decision-making branches; the recognized gestures being semantically converted, and the human-computer interaction interface operating according to the result of semantic conversion. The best granularity is obtained by weighted summation, and the best granularity is used as the finest granularity. The invention not only can more accurately recognize gestures in the cockpit and execute gesturecommands, but also can reduce the interaction time of the cockpit man-machine interaction interface and provide more comfortable interaction experience for users.

Description

technical field [0001] The invention belongs to the field of intelligent driving, and in particular relates to a method and system for optimizing the human-computer interaction interface of an intelligent cockpit based on three-way decision-making. Background technique [0002] With the development of artificial intelligence and deep learning technology, intelligent driving has attracted the attention of many people. As one of the typical human-computer interaction methods in intelligent driving, gesture recognition is very important to the optimal design of the human-computer interaction (HMI) interface in the cockpit. Accurate and fast gesture recognition can not only provide a more comfortable interactive experience, but also improve the safety of drivers. [0003] Current gesture recognition methods mainly include sensor-based devices and computer vision-based methods. Although the former has a better recognition rate, its cost is relatively high, and the interactive e...

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): G06F3/01G06K9/00
CPCG06F3/011G06F3/017G06V40/28G06V40/113
Inventor 刘群张刚强王如琪
Owner CHONGQING UNIV OF POSTS & TELECOMM
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