Human body fall detection method and device
A detection method and detection device technology, applied in image data processing, image enhancement, instruments, etc., can solve the problems of decreased detection rate of human body, low detection rate and accuracy of human fall events, and difficult acquisition
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Embodiment 1
[0076] In the actual scene, when the human body falls, the speed of the human body itself will undergo a large change, that is, the human body will suddenly accelerate. In addition, in the top-mounted scene of the video equipment, when the human body falls, the area of the area corresponding to the head or shoulder in the image will also change accordingly, that is, in the video image collected under the top-mounted scene, The area of the area corresponding to the head or shoulders will be reduced due to a fall of the human body. Therefore, in the top-mounted scene of the video equipment, by combining the area of the area corresponding to the head or shoulder in the captured video and the movement speed of the human body, it can be judged whether the human body has fallen.
[0077] Therefore, according to an aspect of the embodiments of the present invention, a method for detecting a human body fall is provided, which is applied to a top-mounted scene of an image acquisi...
Embodiment 2
[0093] Specifically, in the embodiment of the present invention, a method for detecting a human fall is disclosed, and the corresponding sequence flow chart of the method is as follows figure 2 As shown, that is, in the top-mounted scene of the image acquisition device, after acquiring the image of the current frame, perform the following steps:
[0094] Step S201: Obtain the first area of the target to be detected determined by the body recognition part in the current frame image;
[0095] Step S202: Obtain the current speed of the target to be detected;
[0096] Step S203: Judging whether the current speed of the target to be detected is greater than the speed range of the normal movement of the target to be detected, if the judgment result is yes, execute step S205, and if the judgment result is no, execute step S204;
[0097] Step S204: Continue to track the target to be detected;
[0098] Step S205: Judging whether the area of the first area is smaller than the cor...
Embodiment 3
[0104] Based on the above embodiments, the embodiment of the present invention specifically describes the process of determining the first region of the object to be detected in the current frame image determined by the body recognition part in step S101.
[0105] Wherein, step S101 specifically includes:
[0106] Extract the region corresponding to at least one human body recognition part of the target to be detected from the current frame image through the feature extraction layer of CNN;
[0107] Mapping the extracted area corresponding to at least one human body recognition part to the current frame image through the feature mapping layer of CNN to generate a rectangular target frame;
[0108] The rectangular target frame is determined as the first area where the target to be detected is determined by the body recognition part.
[0109] CNN is a feed-forward neural network, artificial neurons can respond to surrounding units, and can process large images. Generally, the ...
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Abstract
Description
Claims
Application Information
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