Deep learning-based human body motion recognition method of multi-channel image feature fusion

A human action recognition and image feature technology, applied in character and pattern recognition, instruments, biological neural network models, etc., can solve the problems of small inter-class differences, inability to realize high-precision human action recognition, and large internal differences

Inactive Publication Date: 2018-07-17
SOUTH CHINA UNIV OF TECH
View PDF2 Cites 60 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Although the traditional method based on manual features and the method based on deep learning have achieved good classification performance in human action recognition, due to the complexity of human action, the interference of background factors i

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
  • Deep learning-based human body motion recognition method of multi-channel image feature fusion
  • Deep learning-based human body motion recognition method of multi-channel image feature fusion
  • Deep learning-based human body motion recognition method of multi-channel image feature fusion

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0052] Such as Figure 1 to Figure 2 As shown, the human action recognition method based on the fusion of multi-channel image features of deep learning in the present invention is used to identify the human action in the video; including the following four steps:

[0053] (1) Extract the original RGB picture from the video, and calculate the dynamic map and optical flow map of the segmented video through the RGB picture;

[0054] (2) Carry out cropping operation to the input picture and amplify the training data set;

[0055] (3) Construct a three-channel convolutional neural network, and input the finally obtained video clips into the three-channel convolutional neural network for training to obtain a corresponding network model;

[0056] (4) For the video clip to be recognized, extract the original RGB image, and calculate its corresponding dynamic graph and optical flow graph, use the three-channel convolutional neural network trained in (3) to extract features, and obtain...

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 deep learning-based human body motion recognition method of multi-channel image feature fusion. The method comprises: (1) extracting original RGB pictures from videos, and calculating dynamic graphs and optical flow graphs of the segmented videos through the RGB pictures; (2) carrying out cropping operations on the input pictures to expand a training data set; (3) constructing a three-channel convolutional-neural-network, and respectively inputting lastly obtained video segments into the three-channel convolutional-neural-network to carry out training to obtain a corresponding network model; and (4) for a to-be-recognized video segment, extracting original RGB pictures, calculating dynamic graphs and optical flow graphs corresponding thereto, and obtaining a recognition result of a final motion category. According to the method, the three-channel convolutional-neural-network is utilized for learning essential features of data for original input of different morphologies, multi-channel dense fusion operations are carried out on the data of the three morphologies in the middle of the network, expression ability of the features is improved, and purposes of multi-channel information sharing and a high accuracy degree are achieved.

Description

technical field [0001] The present invention relates to the technical field of image processing and analysis, and more specifically, relates to a human action recognition method based on multi-channel image feature fusion based on deep learning. Background technique [0002] Human action recognition in video refers to a technology for human action recognition and classification by analyzing and processing visual feature information in video. This technology is widely used in intelligent video surveillance, behavior analysis, video retrieval and so on. Traditional human action recognition is based on manually designed feature training classifiers for action classification. At present, the strategy with the best effect of the traditional method is to extract features based on the improved dense trajectory (improved Dense Trajectory, iDT), combined with Fisher Vector (Fisher Vector, FV) modeling to identify human body work. In recent years, with the rapid development of deep ...

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): G06K9/00G06K9/62G06N3/04
CPCG06V40/23G06V20/40G06N3/045G06F18/241G06F18/253G06F18/214
Inventor 张见威钟佳琪
Owner SOUTH CHINA UNIV OF TECH
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