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

Quantized transition change detection for activity recognition

A technology of human activities and categories, applied in the field of identifying human activities, can solve the problems of low efficiency in identifying human activities

Pending Publication Date: 2022-02-01
에버씬리미티드
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Because each person has a different body structure, different body shape, different skin color, etc., the traditional human activity recognition technology is very inefficient in identifying human activities

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
  • Quantized transition change detection for activity recognition
  • Quantized transition change detection for activity recognition
  • Quantized transition change detection for activity recognition

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0018] The following detailed description illustrates embodiments of the disclosure and how they can be implemented. While certain modes for carrying out the disclosure are disclosed, those skilled in the art will recognize that other embodiments for carrying out or practicing the disclosure are possible.

[0019] figure 1 An environment 100 is shown in which various embodiments of the present disclosure may be practiced. The environment 100 includes an imaging device 101 , an activity recognition system 102 , and a computing device 103 communicatively coupled to each other via a communication network 104 . The communication network 104 may be any suitable wired network, wireless network, combination of these networks, or any other conventional network without limiting the scope of the present disclosure. A few examples may include local area networks (LANs), wireless LAN connections, Internet connections, point-to-point connections, or other network connections and combinat...

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

A system for recognizing human activity from a video stream includes a classifier for classifying an image frame of the video steam in one or more classes and generating a class probability vector for the image frame based on the classification. The system further includes a data filtering and binarization module for filtering and binarizing each probability value of the class probability vector based on a pre-defined probability threshold value. The system furthermore includes a compressed word composition module for determining one or more transitions of one or more classes in consecutive image frames of the video stream and generating a sequence of compressed words based on the deter-mined one or more transitions. The system furthermore includes a sequence dependent classifier for extracting one or more user actions by analyzing the sequence of compressed words to and recognizing human activity therefrom.

Description

technical field [0001] This disclosure relates generally to artificial intelligence, and more particularly to recognizing human activity from video streams and symbol processing. Background technique [0002] With the advancement of technology, human activity recognition has become extremely important. Human activity recognition helps in various applications such as monitoring the checkout process in retail stores involving self-checkout (SCO) systems. Such systems allow buyers to complete the purchasing process themselves. Another application example of human motion recognition is to assist video surveillance by detecting unfair activities by shoplifters, such as theft, thereby alerting personnel employed in the shop to prevent theft. In addition, human activity recognition is also used in intelligent driving assistance systems, assisted living systems for people in need, video games, and physical therapy, among others. In addition, human activity recognition is also act...

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): G06V40/20G06V10/764G06K9/62G06V10/771
CPCG06V40/20G06V20/41G06V10/82G06V10/771G06V10/764G06F18/2113G06V20/40G06V10/776G06V10/778G06V40/10G06F18/24G06F18/217
Inventor D·佩斯卡鲁C·塞尔纳扎努-格拉万V·圭
Owner 에버씬리미티드
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