Fall behavior identification method based on human skeleton key point detection
A recognition method and key point technology, which is applied in the field of fall behavior recognition based on human skeleton key point detection, can solve the problems that manpower cannot effectively supervise the construction site, the construction site is complicated, and the safety supervisor cannot be on guard all the time. The effect of high reliability, reduced computing cost, and increased robustness
Pending Publication Date: 2022-01-11
浙江汉德瑞智能科技有限公司
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Problems solved by technology
In order to ensure the safety of construction workers and other personnel entering the construction site as much as possible, safety supervisors are required to supervise. However, due to the complexity of the construction site, manpower cannot effectively supervise the entire construction site, and safety supervisors cannot always stay on the construction site. Supervision, on the construction site, detects the worker's fall action, and can respond in time to ensure the safety of the worker and obtain timely assistance
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[0040]In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.
[0041] On the contrary, the invention covers any alternatives, modifications, equivalent methods and schemes within the spirit and scope of the invention as defined by the claims. Further, in order to make the public have a better understanding of the present invention, some specific details are described in detail in the detailed description of the present invention below. The present invention can be fully understood by those skilled in the art without the description of these detailed parts.
[0042] The present invention first carries out following definition and description:
[0043] LSTM: Lo...
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The invention discloses a fall behavior identification method based on human skeleton key point detection. The method comprises the following steps: S10, collecting data: acquiring a human skeleton joint point sequence by using OpenPose; S20, labeling data, and organizing a data set; S30, building a GC-LSTM model, and performing model training by using an existing data set; and S40, judging whether to give an alarm or not. According to the invention, skeleton data is obtained through human skeleton posture detection, and then a trained convolutional neural network is used for classification and recognition to make a fall judgment; and, according to the method, tumble identification is realized by using model behaviors based on OpenPose skeleton acquisition and a graph convolution long-short-term memory neural network (GC-LSTM).
Description
technical field [0001] The invention belongs to the technical field of motion detection, and relates to a fall behavior recognition method based on human skeleton key point detection. Background technique [0002] Human behavior recognition based on deep learning is a major research hotspot in the field of computer vision. Among them, the deep neural network algorithm based on key point detection can realize the automatic detection of human skeleton posture, such as joint structure, gesture, expression state, etc., and It can identify human behavior types by describing human skeleton information through key points. The human body can be regarded as a system composed of bones and joints connecting the bones, so that the behavior of the human body can be represented by the joint points and the correlation between joints, and the spatio-temporal characteristics. From the classification of the properties of the input image, it can be divided into Keypoint detection based on dep...
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IPC IPC(8): G06V10/46G06V40/10G06V10/82G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06N3/044
Inventor 张继勇舒洪睿朱晨薇
Owner 浙江汉德瑞智能科技有限公司