Human body action recognition method based on skeleton key point detection

A human action recognition and action recognition technology, applied in the field of action recognition, can solve the problems of difficult operation and poor portability of the recognition method, and achieve the effects of strong anti-irritability, simple operation and strong portability.

Pending Publication Date: 2020-11-10
XIAN UNIV OF TECH
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Problems solved by technology

[0004] The purpose of the present invention is to provide a human body action recognition method based on bone key point detec

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  • Human body action recognition method based on skeleton key point detection
  • Human body action recognition method based on skeleton key point detection
  • Human body action recognition method based on skeleton key point detection

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[0028] The present invention will be described in detail below in conjunction with the drawings and specific embodiments.

[0029] The present invention is a new technology for detecting key points. The method for detecting key points of human bones based on deep learning effectively fuses information of different scales and tasks, so that the way of information fusion develops from flat to three-dimensional, which is critical to human bones. The model development of point detection has practical significance.

[0030] The present invention is a human action recognition method based on bone key point detection, such as figure 1 As shown, follow the steps below:

[0031] Step 1. Obtain multiple video segments of each standard action of the computer vision system, and perform data preprocessing on each video segment;

[0032] The specific process of collecting multiple video segments for each standard action is: collecting two people doing the same standard action at three different ang...

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Abstract

The invention discloses a human body action recognition method based on skeleton key point detection, and the method comprises the steps: obtaining a plurality of video segments of each standard action of a computer vision system, and carrying out data preprocessing of each video segment; carrying out key point information extraction on each frame of image of each video segment after data preprocessing by adopting a key point detection network to form a plurality of key feature vectors of each video segment; establishing a pre-action recognition model corresponding to each video segment according to the plurality of key feature vectors of each video segment; extracting real-time key point information of the real-time human body motion video by using a key point detection network to generate a real-time key feature vector; adopting a human body motion recognition algorithm to recognize human body motions. The method is high in recognition precision, a weighted K-nearest neighbor algorithm is used for recognizing human body actions, key feature vectors which are more balanced and higher in anti-noise capacity are obtained by endowing all distance points with different weights, and therefore a final recognition result is more accurate.

Description

technical field [0001] The invention belongs to the technical field of motion recognition, and in particular relates to a human body motion recognition method based on skeleton key point detection. Background technique [0002] Human action recognition has always been a popular research direction in computer vision, artificial intelligence and pattern recognition, and has a wide range of applications in the fields of human-computer interaction, virtual reality, video retrieval and security monitoring. The existing human action recognition methods mainly include the human action recognition method based on wearable inertial sensors and the human action recognition method based on computer vision. The human body action recognition method based on wearable sensors collects the action information of the human body through sensors fixed on specific parts of the human body, and transmits it to the computer through a wireless transmission module, and then performs preprocessing, fe...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/20G06F18/24147G06F18/214
Inventor 罗作民郭洪博丁翠
Owner XIAN UNIV OF TECH
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