Fall detection method based on residual bidirectional SRU network
A detection method and residual technology, applied in the field of behavior recognition, can solve the problem of not fully reflecting the correctness of action classification
Pending Publication Date: 2021-05-18
CHINA THREE GORGES UNIV
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Therefore, the final trained model will not fully reflect the correctness of action classification
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[0035] A fall detection method based on a residual bidirectional SRU network, comprising the following steps:
[0036] Step 1: Divide human behaviors into falling, running, jumping, walking, standing, and lying down, collect image data of human behaviors, and form a sample data set of behaviors;
[0037] Step 2: Build a residual two-way SRU network for behavior detection;
[0038] Step 3: Use balanced simple and easy-to-segment samples to manipulate the focus loss function and optimize the residual bidirectional SRU network;
[0039] Step 4: Use the sample data set to train and test the residual bidirectional SRU network to achieve detection accuracy;
[0040] Step 5: Collect real-time images of people, input the trained residual bidirectional SRU network, and detect whether there is a fall behavior.
[0041] Such as figure 1 As shown, the residual SRU network unit of the residual bidirectional SRU network includes the first bidirectional SRU layer, the second bidirectional...
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The invention discloses a fall detection method based on a residual bidirectional SRU network, and the method comprises the steps: collecting image data of human behaviors and actions, and forming a sample data set of the behaviors and actions; constructing a residual bidirectional SRU network for behavior and action detection; adopting a balance simple and easy-to-separate sample control focus loss function, and optimizing the residual bidirectional SRU network; training and testing the residual bidirectional SRU network by using the sample data set to enable the residual bidirectional SRU network to reach detection precision; and acquiring a real-time image of a person, inputting the real-time image into the trained residual bidirectional SRU network, and detecting whether a falling behavior exists or not. The invention provides a new neural network model, namely a residual bidirectional SRU network, which is used for detecting the falling behavior of a person, and compared with an existing neural network model, the model has the advantages of high convergence speed, higher stability and higher precision and accuracy; the residual bidirectional SRU network solves the problem of gradient disappearance and is easy to train.
Description
technical field [0001] The invention belongs to the field of behavior recognition, and in particular relates to a fall detection method based on a residual bidirectional SRU network. Background technique [0002] With the rapid development of science and technology, artificial intelligence has been known to more and more people, and artificial intelligence is more and more widely used in people's lives, and the deep learning technology is particularly hot. Behavior recognition technology, as a sub-problem in deep learning, is very important in people's daily life. It is helpful to identify dangerous behaviors such as falls of the elderly, and plays a very important role in elderly care. [0003] In the field of intelligent recognition, people use different methods to classify and recognize human body movements according to different behaviors of the human body, such as methods based on wearable devices, posture estimation, and video positioning, and design different network ...
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Login to View More IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06V40/20G06N3/045G06F18/2415G06F18/214
Inventor 陈小辉孟登熊昕陈凌俊
Owner CHINA THREE GORGES UNIV