Method and system for estimating and tracking human body posture in video

A technology in human body posture and video, applied in the field of image processing and computer vision, can solve problems such as complex models, achieve the effect of improving accuracy, accurate estimation of human body posture, and inaccurate estimation of image blur

Active Publication Date: 2021-08-13
青岛根尖智能科技有限公司
View PDF6 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the research on the human pose tracking method combined with optical flow estimation not only involves deep learning, image-based human pose estimation, etc. The combined model is relatively complex, so there are few literatures that comprehensively consider this part of the research

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 estimating and tracking human body posture in video
  • Method and system for estimating and tracking human body posture in video
  • Method and system for estimating and tracking human body posture in video

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0030] like figure 1 As shown, a human pose estimation and tracking method in a video, including:

[0031] Step (1) collects the video data in the human pose estimation data set;

[0032] Step (2) performing human body pose estimation on each frame image in the video data through the human body pose estimation network model to obtain estimated data, and obtaining optical flow estimation results between adjacent frames through an optical flow estimation algorithm;

[0033] Step (3) Input N frames of estimated data and the results of optical flow estimation between adjacent frames, a total of N+(N-1) parameters, into the improved CNN network model for human body pose tracking training, and perform overlapping based on loss function constraints Frame rectification outputs accurate human pose estimation and tracking data. where N≧2.

[0034] Further, it also includes preprocessing the video data, performing preprocessing on the video data to obtain a single frame image, and per...

Embodiment 2

[0049] A human body pose estimation and tracking system in video, comprising:

[0050] a data collection module, configured to collect video data in the human pose estimation dataset;

[0051] The data processing module is configured to perform human pose estimation for each frame of images in the video data through the human pose estimation network model to obtain estimation data, and obtain the optical flow estimation result between adjacent frames through an optical flow estimation algorithm;

[0052] The data correction module is configured to input N frames of estimated data and the optical flow estimation results between adjacent frames, a total of N+(N-1) parameters into the improved CNN network model for human pose tracking training, and based on the loss function Constrained overlapping frame correction outputs accurate human estimated pose and tracking data.

[0053] Further, the specific configurations of the data acquisition module, the data processing module and ...

Embodiment 3

[0055] A computer-readable storage medium for storing computer instructions, when the computer instructions are executed by a processor, the method for estimating and tracking a human body pose in a video as described in the above embodiments is completed.

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 method and a system for estimating and tracking a human body posture in a video. The method comprises the following steps: acquiring video data in a human body posture estimation data set; performing human body posture estimation on each frame of image in the video data through the human body posture estimation network model to obtain estimation data, and obtaining an optical flow estimation result between adjacent frames through an optical flow estimation algorithm; inputting N + (N-1) parameters into an improved CNN network model to carry out human body posture tracking training, and carrying out overlapped frame correction based on loss function constraint to output accurate human body estimation posture and tracking data; the method can effectively improve image blurring attitude estimation inaccuracy caused by rapid motion of a human body in a video, and improves the human body attitude estimation and tracking accuracy in the video.

Description

technical field [0001] The present disclosure relates to the fields of image processing and computer vision, and in particular relates to a human body pose estimation and tracking method model in video combined with optical flow estimation. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] In recent years, on the basis of human body pose estimation based on deep learning, research on topics such as multi-person pose estimation and human behavior recognition has gradually become a research hotspot, especially in virtual reality, video analysis, identity verification, somatosensory interaction, human-computer interaction, and intelligence. There are broad application prospects in monitoring, medical diagnosis and other fields, and deep learning methods have become a tool that people use every day. As the computer vision community shifts fro...

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): G06K9/00G06N3/04G06N3/08G06T7/246G06T7/269
CPCG06T7/246G06T7/269G06N3/08G06T2207/10016G06T2207/20081G06T2207/20084G06V40/20G06V20/42G06V20/46G06N3/045Y02T10/40
Inventor 王海滨纪文峰
Owner 青岛根尖智能科技有限公司
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