Tumble behavior analysis and detection method based on double-flow convolutional neural network

A convolutional neural network and behavior analysis technology, applied in the field of fall behavior analysis and detection, can solve the problems of unachievable speed and accuracy, unsatisfactory neural network effect, etc., achieve less calculation, reduce hardware equipment cost, and broad The effect of market prospects

Inactive Publication Date: 2020-09-04
NANJING UNIV OF POSTS & TELECOMM
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the effect of single-stream neural network in fall detection is not ideal, and the artificially de

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
  • Tumble behavior analysis and detection method based on double-flow convolutional neural network
  • Tumble behavior analysis and detection method based on double-flow convolutional neural network
  • Tumble behavior analysis and detection method based on double-flow convolutional neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0035] The present invention will be described in further detail below in conjunction with the accompanying drawings. The present invention provides a fall behavior analysis and detection method based on a two-stream convolutional neural network, such as figure 1 As shown in , it is a schematic diagram of the analysis and detection model architecture, which specifically includes the following steps:

[0036] Step 1: Extract pedestrian profile images and motion history maps to form a falls dataset.

[0037] Motion History Image (MHI) is a grayscale image that can represent motion information. By calculating the change of pixel grayscale value at the same position within a time period, the target motion is expressed in the form of image grayscale value. The higher the gray value of the pixel, the closer the motion occurred. Therefore, the motion history map contains the target motion direction information. Since the gradient direction corresponds to the direction in which 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 discloses a tumble behavior analysis and detection method based on a double-flow convolutional neural network, and the method comprises the steps: firstly extracting a pedestrian contourimage and a motion history image to form a tumble data set; secondly, on the tumble data set, determining an optimal double-flow convolutional neural network structure, a spatial flow channel input contour image and a time flow channel input motion history graph by using a neural network search model; and finally, performing tumble judgment through a fusion module. The network structure adopted by the invention is the network structure with the optimal balance between precision and delay, the calculated amount caused by a redundant structure is greatly reduced, the hardware equipment cost isreduced, the analysis and detection of the falling behavior can be realized on the intelligent terminal, and the market prospect is wide.

Description

technical field [0001] The invention belongs to the field of smart home, and in particular relates to a fall behavior analysis and detection method based on a dual-stream convolutional neural network. Background technique [0002] The traditional fall behavior recognition algorithm is to manually design and extract features, and then use threshold method or machine learning algorithm to classify. However, the artificially designed features are often not comprehensive enough to accurately describe the target. Moreover, the traditional fall behavior detection needs to wear devices, such as accelerometers, gyroscopes, etc., and the operation is relatively complicated. In addition, traditional fall detection methods are difficult to scale well with more data and have poor adaptability. The development of deep learning and computer vision technology provides new solutions for fall detection. For example, convolutional neural networks can extract target features through convolut...

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): G06K9/00G06N3/04G06N3/08
CPCG06N3/08G06V40/23G06N3/045
Inventor 张晖赵前龙赵海涛孙雁飞倪艺洋朱洪波
Owner NANJING UNIV OF POSTS & TELECOMM
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