Off-duty detection method and device and storage medium

A detection method and technology to be detected, applied in neural learning methods, biological neural network models, office automation, etc., can solve the problems of low detection accuracy, high cost, and high computing resources.

Active Publication Date: 2019-11-12
思百达物联网科技(北京)有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Embodiments of the present disclosure provide an off-duty detection method, device, and storage medium to at least solve the problem...

Method used

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  • Off-duty detection method and device and storage medium
  • Off-duty detection method and device and storage medium
  • Off-duty detection method and device and storage medium

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Experimental program
Comparison scheme
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Embodiment 1

[0024] figure 1 A schematic flow chart of the method for leaving work detection according to this embodiment is shown.

[0025] refer to figure 1 As shown, the off-duty detection method of the present embodiment includes the following steps:

[0026] S102: Obtain a picture containing a human body image of an object to be detected;

[0027] S104: Using a feature extraction model based on machine learning training, determine the position information of the human body image in the picture; and

[0028] S106: According to the location information, determine whether the object to be detected leaves the post.

[0029] Specifically, a human body detection model based on a convolutional neural network is used to determine a human body image region including a human body target in an image to be detected. Then, the departure detection model based on the convolutional neural network is used to determine whether the human body target in the human body image area is in the off-duty st...

Embodiment 2

[0078] Figure 6 A schematic diagram of the departure detection device 600 described in this embodiment is shown. The off-duty detection device 600 of this embodiment corresponds to the method according to Embodiment 1.

[0079] refer to Figure 6 As shown, the device 600 includes: an acquisition module 610 for acquiring a picture of a human body image containing an object to be detected; a determination module 620 for determining the position information of the human body image in the picture by using a feature extraction model based on machine learning training and a judging module 630, configured to judge whether the object to be detected leaves the post according to the location information.

[0080] Optionally, the determining module 620 includes: a generating submodule, configured to generate a feature vector corresponding to the picture by using a neural network-based feature extraction model, wherein the first part of the feature vector elements is used to describe t...

Embodiment 3

[0086] Figure 7 A schematic diagram of the departure detection device 700 described in this embodiment is shown. The departure detection device 700 of this embodiment corresponds to the method according to Embodiment 1.

[0087] refer to Figure 7 As shown, the device 700 includes: a processor 710; and a memory 720, connected to the processor 710, for providing the processor 710 with instructions for processing the following processing steps: acquiring a picture containing a human body image of an object to be detected; utilizing a machine-based Learn and train the feature extraction model to determine the position information of the human body image in the picture; and according to the position information, determine whether the object to be detected has left the post.

[0088] Optionally, using a feature extraction model based on machine learning training to determine the position information of the human body image in the picture includes: using a neural network-based fe...

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PUM

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Abstract

The invention discloses an off-duty detection method and device and a storage medium. The off-duty detection method comprises the steps of obtaining a picture of a human body image containing a to-be-detected object; utilizing a feature extraction model based on machine learning training to determine position information of the human body image in the picture; and judging whether the to-be-detected object leaves the post or not according to the position information. The off-duty detection method provided by the invention is relatively comprehensive in identification, high in accuracy and highin identification speed, and has relatively low time delay, so that real-time monitoring is ensured. In addition, a feature extraction model based on machine learning training is adopted in the scheme; therefore, compared with a traditional detection method, whether a plurality of employees are on duty or not can be detected only by using a single camera, if a plurality of employees are in one camera, the employees in the range only need to mark out the position range of the employees when the position range is divided, the cost is low, the use is convenient, and no complex wearable equipmentis used.

Description

technical field [0001] The present application relates to the field of behavior detection methods, in particular to an off-duty detection method, device and storage medium. Background technique [0002] There are many positions in our daily life that must be guarded in real time, such as sentry, door guard on duty, radar alert, hospital on duty, border guard, etc. However, with the advancement of social level, human living standards are gradually improving, and human resources The cost is getting higher and higher. In the past, the traditional method of checking whether the personnel on the job was on the job was to find a person and visit the job from time to time. Resource overhead is also relatively large. There are also some more advanced methods for detection, such as adding wearable devices, but these methods are also relatively cumbersome and require wearing fixed devices every day. [0003] At present, the off-the-job detection methods and the main problems in the ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08G06Q10/10
CPCG06N3/08G06Q10/105G06V40/10G06V20/593G06N3/045G06F18/241
Inventor 胡泽双田志博
Owner 思百达物联网科技(北京)有限公司
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