Safety monitoring method and device, storage medium, electronic equipment and air conditioner

A security monitoring and first-time technology, applied in the direction of still image data retrieval, instruments, biological neural network models, etc., can solve the problems of automatic identification of crimes, inability to identify camouflage, low identification rate, etc., to achieve enhanced behavior monitoring and The effect of intelligent behavior classification, improved recognition ability, and improved efficiency

Pending Publication Date: 2020-11-03
GREE ELECTRIC APPLIANCES INC +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to the limitations of camera technology, it usually leads to low recognition rate, inability to recognize camouflage, and the need to manually identify whether there is criminal behavior, so that automatic identification of crimes and other behaviors cannot be achieved, and the effect of intelligent security monitoring

Method used

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  • Safety monitoring method and device, storage medium, electronic equipment and air conditioner
  • Safety monitoring method and device, storage medium, electronic equipment and air conditioner
  • Safety monitoring method and device, storage medium, electronic equipment and air conditioner

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0051] see figure 1 as shown, figure 1 A schematic flowchart of a security monitoring method provided by an embodiment of the present invention is shown.

[0052] Step S101: Acquiring image data.

[0053] Step S102: Input image data into a pre-trained first convolutional neural network model.

[0054] Step S103: Extracting the body shape features in the image data based on the first convolutional neural network model and judging whether the body shape features match the data in the security personnel body shape feature database.

[0055] Step S104: When the recognition result output by the first convolutional neural network model is unmatched, input the unmatched image data into the pre-trained second convolutional neural network model.

[0056] Step S105: Using the second convolutional neural network model to perform behavior feature extraction and behavior classification on the unmatched image data to obtain behavior classification results, which include criminal and norm...

Embodiment 2

[0079] see Figure 4 as shown, Figure 4 A schematic flowchart of a security monitoring method provided by another embodiment of the present invention is shown, including steps S201 to S209.

[0080]Wherein, Step S201 to Step S205 may be the same as Step S101 to Step S105 in Embodiment 1, and for the sake of brevity, details are not repeated here, please refer to Embodiment 1 for details. In this embodiment, the description will focus on step S206 to step S209 of judging whether to trigger a safety warning based on the behavior classification result.

[0081] Step S206: Obtain multi-frame image data in the second time period and the third time period and the behavior classification results corresponding to the second time period and the third time period respectively from the first time period along time, the first time period, The second time period and the third time period constitute a behavior classification collection time period.

[0082] Step S207: Determine whether ...

Embodiment 3

[0092] see Figure 5 as shown, Figure 5 A schematic structural diagram of a safety monitoring device provided by an embodiment of the present invention is shown. It includes:

[0093] Image acquisition module 51, which is used to acquire image data;

[0094] An image input module 52, which is used to input image data into a pre-trained first convolutional neural network model;

[0095] Data matching module 53, it is used to extract the body shape feature in the image data based on the first convolutional neural network model and judges whether the body shape feature matches the data in the security personnel body shape feature library;

[0096] Data input module 54, it is used for when the identification result that the first convolutional neural network model outputs is unmatched, the image data that does not match is input into the second convolutional neural network model that has been trained in advance;

[0097] Behavior classification module 55, which is used to use...

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Abstract

The invention discloses a safety monitoring method and device, a storage medium, electronic equipment and an air conditioner, and the method comprises the steps: obtaining image data, and inputting the image data into a pre-trained first convolutional neural network model; extracting body shape features in the image data by using a first convolutional neural network model and judging whether the body shape features are matched with data in a safety personnel body shape feature library or not; when the recognition result output by the first convolutional neural network model is mismatching, inputting the mismatched image data into a pre-trained second convolutional neural network model, and performing behavior feature extraction and behavior classification on the mismatched image data by using the second convolutional neural network model to obtain a behavior classification result, wherein behavior classification results may include crimes and normalities. The method can improve the monitoring and recognition capability, achieves the recognition of the unsafe persons, improves the behavior monitoring and intelligent behavior classification of the unsafe persons, and greatly improvesthe safety monitoring efficiency.

Description

technical field [0001] The invention relates to the technical field of image detection, in particular to a safety monitoring method, device, storage medium, electronic equipment and air conditioner. Background technique [0002] With the development of technology and the improvement of living standards, people's awareness of the safety monitoring of their living space is gradually increasing. In the prior art, a camera is usually used to monitor the target space safely, so that the video data containing the criminal behavior or the object of the criminal behavior can be stored for subsequent use or for real-time monitoring. However, due to the limitations of camera technology, it usually leads to low recognition rate, inability to recognize camouflage, and the need to manually identify whether there is criminal behavior, so that it is impossible to automatically identify crimes and other behaviors and perform intelligent security monitoring. Contents of the invention [0...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/00G06N3/04G06F16/53
CPCG06F16/53G06V40/20G06V20/52G06N3/045G06F18/241G06F18/214
Inventor 刘红铮宋德超陈翀
Owner GREE ELECTRIC APPLIANCES INC
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