Falling detection method based on deep convolutional network

A deep convolution and detection method technology, applied in the field of computer vision and deep learning, can solve the problems of low detection accuracy and false positives, and achieve the effect of solving the occlusion relationship, improving the accuracy and improving the transmission.

Active Publication Date: 2018-11-13
ZHENGZHOU UNIV
View PDF3 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, it has a big disadvantage of detecting the pressure of objects inside and around the object and generating false positives in case of fall detection, which leads to low detection 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
  • Falling detection method based on deep convolutional network
  • Falling detection method based on deep convolutional network
  • Falling detection method based on deep convolutional network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0045] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the present invention Examples, not all examples. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0046] figure 1 A schematic diagram of the network framework of the deep convolutional network provided by the embodiment of the present invention; as figure 1 As shown, the deep convolutional network is divided into three stages, and each stage has two branches: the first branch (JOINTS) is used to predict the confidence map corresponding to each body part; the second branch (B...

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 provides a falling detection method based on a deep convolutional network. The method comprises the following steps that: S1: according to an image collected by a monocular camera, adopting the deep convolutional network to calculate the body joint confidence map of an object to be detected in the image; S2: adopting a bidirectional map structure information model to calculate the optimal gesture configuration set of all body positions in the body joint confidence map, wherein the optimal gesture configuration set is used for showing distance between each body position and a preset horizontal position; and S3: according to the between each body position and the preset horizontal position, judging whether the object to be detected falls or not. By use of the method, a multi-stage deep convolutional network is adopted to analyze the image so as to better extract the characteristic information of the image, shorten operation time and improve detection accuracy.

Description

technical field [0001] The invention relates to the technical fields of computer vision and deep learning, in particular to a fall detection method based on a deep convolutional network. Background technique [0002] Falls are a significant cause of fatal injuries, especially among older adults, and pose a serious obstacle to independent living. Hence, the healthcare industry has increased demand for monitoring systems, especially for fall detection systems, owing to the rapid increase in the number of elderly people. [0003] Recently, fall detection methods are mainly divided into three categories: wearable-based, ambient-device-based detection methods, and vision-based methods. Wearable device-based methods rely on clothing with embedded sensors to detect the motion and position of a subject's body; ambient device-based detection methods attempt to fuse audio and visual data and event sensing through vibration data. Most environmental device-based detection methods use ...

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 Applications(China)
IPC IPC(8): G06K9/00G08B21/04
CPCG08B21/0476G08B21/043G06V40/23
Inventor 王菁周兵吕培
Owner ZHENGZHOU 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