Falling action detection method based on key skeleton point track analysis

A technology of trajectory analysis and motion detection, applied in the fields of instruments, character and pattern recognition, computer parts, etc., can solve problems such as user unwillingness to accept

Active Publication Date: 2019-09-27
SHENYANG AEROSPACE UNIVERSITY
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Problems solved by technology

But the privacy problem is a fatal shortcoming, and most users are unwilling to accept it

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  • Falling action detection method based on key skeleton point track analysis
  • Falling action detection method based on key skeleton point track analysis
  • Falling action detection method based on key skeleton point track analysis

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Embodiment

[0040] Such as figure 1 As shown, a fall action detection method based on key bone point trajectory analysis includes the following steps:

[0041]Step A1, collecting a large number of people's videos (image sequences) for OpenPose neural network model training:

[0042] 1.1 First collect a large number of videos of normal daily activities of people, which can be single or multiple people, and manually mark the key skeleton points of each frame of the image in the video;

[0043] 1.2 Get the coordinates of the bone points, and record them in the order of the same rule as the frame label;

[0044] 1.3 Train the neural network model by inputting a large number of labeled samples like this;

[0045] 1.4 OpenPose uses the features of human key point affinity fields (PAFs) to infer who the bone points belong to, without misassigning the bone points.

[0046] Step A2, collect some fall videos and ADL action videos or image sequences:

[0047] 2.1 Select the available fall detect...

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Abstract

The invention provides a falling action detection method based on key skeleton point track analysis. The falling action detection method comprises the following steps of 1, firstly, collecting a plurality of groups of image sequences including the falling actions and a plurality of groups of image sequences including other actions except the falling actions; 2, extracting the key skeleton points, extracting the related information of the key skeleton points of the human body from the positive and negative samples, wherein the related information comprises the position information and the depth information of each skeleton point in the RGB image; 3, constructing a feature model, and generating a feature descriptor based on a key skeleton point track; and 4, constructing a classifier, classifying the track feature descriptors, and detecting a falling action. According to the present invention, the human skeleton position information is extracted by utilizing the OpenPose human skeleton detection algorithm based on the neural network, the most persuasive human skeleton points can be directly obtained, the method is not limited to the specific conditions and special environments, a user does not need to wear anything, the complex installation is not needed, and the cost is very low.

Description

technical field [0001] The invention relates to the field of computer vision and image recognition, in particular to a fall action detection method based on key bone point track analysis. Background technique [0002] Worldwide, the proportion of the population over the age of 65 continues to increase, and China has become the country with the largest elderly population in the world and one of the countries with the fastest growing aging population. According to United Nations statistics, by the middle of this century, China will have 500 million people over the age of 60, and this number will exceed the total population of the United States. What cannot be ignored is that the problem of empty-nest elderly in China is more prominent. At present, 54% of households in cities are empty-nest elderly families. With the increase in the number of rural migrant workers, the proportion of rural empty-nest elderly is close to half. Due to the increase of age, the physical function of...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/23G06F18/24
Inventor 刘翠微吕健雄杜冲李照奎石祥滨张德园刘芳
Owner SHENYANG AEROSPACE UNIVERSITY
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