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Human body behavior recognition method and device based on residual error network

An identification method and technology of an identification device, which are applied in the field of video identification, can solve the problems of increasing complexity and small amount of calculation, and achieve the effects of reducing time complexity, improving robustness and accurate identification.

Inactive Publication Date: 2017-10-24
ZHEJIANG SCI-TECH UNIV
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AI Technical Summary

Problems solved by technology

In 2D images, the convolution operation is computationally inexpensive, but in 3D video, the complexity of the operation increases exponentially

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  • Human body behavior recognition method and device based on residual error network
  • Human body behavior recognition method and device based on residual error network
  • Human body behavior recognition method and device based on residual error network

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Embodiment Construction

[0052] The technical solution of the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments, and the following embodiments do not constitute a limitation of the present invention.

[0053] The present invention uses convolutional neural network technology in deep learning to extract the time and space features of human behavior in videos, realizes fast and accurate classification of human behavior in videos, and finally builds a new behavior recognition solution and efficient recognition method , so as to improve the scene understanding ability and accuracy.

[0054] Such as figure 1 As shown, the technical solution is a human behavior recognition method based on a residual network, comprising the following steps:

[0055] Step S1, converting the video into an RGB image and an optical flow image.

[0056] In this embodiment, the video to be recognized is converted into an RGB image and an optical flow image. ...

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Abstract

The invention discloses a human body behavior recognition method and device based on a residual error network. The method comprises the steps: converting a video into an RGB image and an optical flow image through opencv, extracting the spatial feature and time feature through a residual error network, carrying out the fusion of two features, inputting the features into a classifier for classification, and determining the class of a behavior of a human body in the video. The device comprises a conversion module, a spatial feature extraction module, a time feature extraction module, a fusion module, and a classification module. The method and device increase the depth of a network, improve the recognition accuracy, and reduce the time complexity of an algorithm.

Description

technical field [0001] The invention belongs to the technical field of video recognition, and in particular relates to a human behavior recognition method based on a residual network. Background technique [0002] With the development and progress of Internet technology, the improvement of network environment, and the popularization of video acquisition equipment such as digital cameras and video recorders, network video, mobile phone video, and surveillance video data have shown explosive growth. In order to meet the challenge of the rapid growth of video data, solve the contradiction between large-scale computing requirements and high-end hardware, massive video data and effective data, the analysis and research of video content is imminent. [0003] The analysis and recognition of human behavior in video is an important part of video content analysis. Human behavior recognition based on video is to process the unknown video sequence collected by the computer, which can a...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/20G06V20/46G06F18/24
Inventor 桂江生迟元峰包晓安
Owner ZHEJIANG SCI-TECH UNIV
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