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A human fall detection method based on rgbd sensor

A detection method and sensor technology, which is applied in the field of human motion recognition, can solve problems such as poor reliability, sensitivity to environmental light changes, and few technical features of fall detection, and achieve the effect of protecting privacy

Active Publication Date: 2020-09-04
SHENZHEN GRADUATE SCHOOL TSINGHUA UNIV
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AI Technical Summary

Problems solved by technology

This type of fall detection technology can extract fewer features and is sensitive to changes in ambient light, so the reliability is not good

Method used

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  • A human fall detection method based on rgbd sensor
  • A human fall detection method based on rgbd sensor
  • A human fall detection method based on rgbd sensor

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Embodiment Construction

[0051]The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, so that the advantages and features of the present invention can be more easily understood by those skilled in the art, so as to define the protection scope of the present invention more clearly. It should be emphasized that the following description is only exemplary and not intended to limit the scope of the invention and its application.

[0052] refer to figure 1 , in one embodiment, the fall detection method based on RGBD sensor includes: color camera calibration and depth camera stereo calibration, spatial three-dimensional structure information extraction, joint point location based on convolutional neural network, joint point static and dynamic feature extraction, fall Judgment classification training and prediction. This example uses Microsoft's KinectV2 as the RGBD sensor.

[0053] Among them, stereo camera calibration includes: ...

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Abstract

The invention discloses a human body fall detection method based on an RGBD sensor, comprising the following steps: performing calibration and correction of internal and external camera parameters on the RGBD sensor; taking calibration steps for RGBD video sequences to extract three-dimensional structure information of the active space environment; based on multi-level convolution The neural network extracts the joint points to obtain a group of three-dimensional coordinate positions of the joint points of the human body; extracts the three-dimensional structure information, static information, and dynamic information of the joint points of the human body as the characteristics describing the human action behavior to comprehensively analyze whether the human body has abnormal fall behavior. The fall detection method of the present invention can accurately and conveniently monitor the three-dimensional motion state of the elderly and judge whether a fall has occurred, and the intelligent RGBD sensor can accurately identify and track the activities of the elderly in real time without affecting the daily life of the elderly. Imaging features can better protect the privacy of the elderly.

Description

technical field [0001] The invention relates to human body action recognition, in particular to a human body fall detection method based on an RGBD sensor. Background technique [0002] Statistics show that my country is gradually entering the stage of an aging society, and how to effectively monitor the safety of the elderly has become an urgent problem to be solved. Falls are the most common safety problem encountered by the elderly in their daily lives. According to the statistics of the World Health Organization, falls are currently the second leading cause of accidental death and unintentional injury death in the world. About 460,000 people die every year due to fall injuries Among them, the proportion of fall injuries in low-income and middle-income countries is more than 80%, and the number of hospital admissions due to fall injuries is as high as 37 million each year. [0003] Falls of the elderly are often accompanied by symptoms such as fractures, soft tissue inju...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G08B21/04G06K9/00G06K9/62
CPCG08B21/043G08B21/0476G06V40/103G06F18/2411
Inventor 袁克虹申代友
Owner SHENZHEN GRADUATE SCHOOL TSINGHUA UNIV
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