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Method and device for daily activity classification under low complexity, equipment and storage medium

A low-complexity, classification-method technology, applied in the field of low-complexity classification methods for daily activities, devices, equipment and storage media, can solve the problems of consuming large computing resources, cloud computing transmission and delay not comparable to edge computing, etc. , to achieve the effect of small computing requirements, low complexity, and solving excessive computing resources

Pending Publication Date: 2021-02-05
SHENZHEN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in the application of intelligent Internet of Things, this method that consumes a large amount of computing resources does not necessarily guarantee real-time computing results.
One solution is to transmit data to the cloud for algorithm calculation, but cloud computing is inferior to edge computing in terms of transmission and delay

Method used

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  • Method and device for daily activity classification under low complexity, equipment and storage medium
  • Method and device for daily activity classification under low complexity, equipment and storage medium
  • Method and device for daily activity classification under low complexity, equipment and storage medium

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Experimental program
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Effect test

Embodiment 1

[0043]figure 1 This is a schematic flow diagram of a low-complexity daily activity classification method provided by an embodiment of the present invention. The low-complexity daily activity classification method provided in this embodiment is suitable for detecting indoor human activities. Device execution, specifically, a low-complexity daily activity classification method provided in this embodiment includes the following steps:

[0044]Step 100: Obtain a radar time-distance map of indoor activities.

[0045]In this embodiment, human activities are tested in an indoor environment. Radars with different signals can be selected for testing according to actual conditions. In this embodiment, the X4M03 radar produced by Novelda is taken as an example. The X4M03 radar module is a pulse Doppler radar that emits a Gaussian envelope. The transmitted signal model is as follows:

[0046]

[0047]Among them, V is the voltage value, t is the fast time, ωcIs the carrier frequency, τ is determined by the ...

Embodiment 2

[0084]Figure 4 This is a schematic flow diagram of a low-complexity daily activity classification method provided by an embodiment of the present invention. The low-complexity daily activity classification method provided in this embodiment is suitable for detecting indoor human activities. Device execution, specifically, the low-complexity daily activity classification method provided in this embodiment includes the following steps:

[0085]Step 200: Obtain a radar time-distance map of indoor activities.

[0086]In this embodiment, human activities are tested in an indoor environment. Radars with different signals can be selected for testing according to actual conditions. In this embodiment, the X4M03 radar produced by Novelda is taken as an example. The X4M03 radar module is a pulse Doppler radar that emits a Gaussian envelope. The transmitted signal model is as follows:

[0087]

[0088]Among them, V is the voltage value, t is the fast time, ωcIs the carrier frequency, τ is determined by th...

Embodiment 3

[0099]The low-complexity daily activity classification device of the embodiment of the present invention can implement the low-complexity daily activity classification method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for the execution method.Figure 5 It is a schematic structural diagram of a low-complexity daily activity classification apparatus 300 in an embodiment of the present invention. ReferenceFigure 5 The low-complexity daily activity classification apparatus 300 provided by the embodiment of the present invention may specifically include:

[0100]The obtaining module 310 is used to obtain a radar time-distance map of indoor activities;

[0101]The extraction module 320 is configured to perform target detection and feature extraction on the radar time-distance map to obtain moving targets;

[0102]The classification module 330 is configured to classify the running target through the trained neural network to o...

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Abstract

The embodiment of the invention discloses a method and device for daily activity classification under a low complexity, equipment and a storage medium. The method comprises the following steps: acquiring a radar time-distance map of indoor activity; performing target detection and feature extraction on the radar time interval graph to obtain a moving target; and classifying the operation targets through a trained neural network to obtain the motion state of the motion target. According to the method for daily activity classification under the low complexity, provided by the embodiment of the invention, human body activity identification is realized by using target detection, feature extraction and a lightweight neural network method, and the problems of excessive consumption of computing resources and no real-time performance for human body activity identification in the prior art are solved; and the effects of low complexity and small calculation requirement in daily activity detection are achieved.

Description

Technical field[0001]The embodiments of the present invention relate to target detection technology, and in particular to a method, device, device, and storage medium for classifying daily activities with low complexity.Background technique[0002]In recent years, with the rapid development of smart IoT applications, various researches based on human body signs have become a hot topic. In daily life, various physical signs of the human body, such as face information, iris information, voice information, gesture information, motion information, etc., contain a large amount of information, which can be used in recognition, positioning, and interaction. Among them, motion information is the focus of the present invention. The daily actions of the human body can intuitively transmit rich information on specific occasions, and play an important role in security monitoring, medical monitoring, human-computer interaction and other fields.[0003]Among various sensors, radar sensors have been w...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S13/50G06K9/62G06N3/04G06N3/08
CPCG01S13/50G06N3/049G06N3/08G06N3/047G06N3/045G06F18/241G06F18/2415
Inventor 阳召成赖佳磊鲍润晗
Owner SHENZHEN UNIV
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