CN110705472A - Off-duty method and system based on video image recognition - Google Patents
Off-duty method and system based on video image recognition Download PDFInfo
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Abstract
The invention provides a method and a system for identifying off duty based on video images, which are used for collecting the video images of stations to be monitored in real time, identifying specific content characteristic labels in video pictures, and recording the appearance time period and/or disappearance time period of each specific content characteristic label; judging whether the target judgment condition is met or not based on the comparison result between the appearance time period and/or the disappearance time period of each specific content characteristic label and the target judgment condition, if so, judging that the station to be monitored is in the off-duty state, otherwise, determining that the station to be monitored is in the normal on-duty state; the off-duty judgment condition library based on the setting can realize the long-term stable supervision of any number of posts and any types of posts, achieve the effect of achieving twice the result with half the effort, reduce the occurrence of production accidents caused by off-duty events and ensure the safety production of enterprises.
Description
Technical Field
The invention relates to the technical field of video identification, in particular to a method and a system for identifying off duty based on video images.
Background
Many jobs require the staff to be on duty, which can be problematic or even result in serious consequences. Many companies now need to arrange specialists to perform uninterrupted supervision and management of these work posts.
However, the supervision of a specially-assigned person has disadvantages, such as increased labor cost, and possible off-duty situations of the supervision person.
Therefore, how to implement the long-term stable supervision on any number of posts and any types of posts is necessary to achieve the effect of achieving twice the result with half the effort.
Disclosure of Invention
The invention provides a method and a system for identifying off duty based on video images, which mainly solve the technical problems that: how to realize the long-term stable supervision on any number of posts and any types of posts.
In order to solve the technical problem, the invention provides a method for identifying off Shift based on a video image, which comprises the following steps:
acquiring a video image of a station to be monitored in real time;
identifying a specific content feature tag in a video picture;
recording the appearance time period and/or disappearance time period of each specific content feature tag;
judging whether the target judgment condition is met or not based on a comparison result between the appearance time period and/or disappearance time period of each specific content feature tag and the target judgment condition;
if so, judging that the station to be monitored is in an off-Shift state;
otherwise, determining that the station to be monitored is in a normal on-duty state;
and the target judgment condition is obtained by acquiring the judgment condition corresponding to the identification information of the station to be monitored from the off-post judgment condition library according to the corresponding relation between the station identification and the judgment condition.
Optionally, the identification information of the station to be monitored includes at least one of a station name and a station number.
Optionally, the specific content feature tag includes at least one of a whole body, a face, and an arm of a person.
Optionally, the off-Shift determination condition library includes at least two sets of determination conditions, where the determination conditions include at least one set of condition factors, and the set of condition factors include a content feature tag and a set time threshold corresponding to the content feature tag.
Optionally, the identifying a specific content feature tag in the video frame includes:
and determining a content feature tag contained in the target judgment condition, and identifying the content feature tag contained in the target judgment condition as the specific content feature tag in the video picture.
The invention also provides a system for identifying off duty based on the video image, which comprises image acquisition equipment and image processing equipment, wherein the image acquisition equipment is in communication connection with the image processing equipment to realize data transmission;
the image acquisition equipment is used for acquiring a video image of a station to be monitored in real time and sending the video image to the image processing equipment in real time;
the image processing equipment is used for receiving the video image in real time, identifying specific content characteristic tags in a video picture, and recording the appearance time period and/or disappearance time period of each specific content characteristic tag; judging whether the target judgment condition is met or not based on the comparison result between the appearance time interval and/or disappearance time interval of each specific content feature label and the target judgment condition, if so, judging that the station to be monitored is in the off-duty state, otherwise, determining that the station to be monitored is in the normal on-duty state; and the target judgment condition is obtained by acquiring the judgment condition corresponding to the identification information of the station to be monitored from the off-post judgment condition library by the image processing equipment according to the corresponding relation between the station identification and the judgment condition.
Optionally, the identification information of the station to be monitored includes at least one of a station name and a station number.
Optionally, the specific content feature tag includes at least one of a whole body, a face, and an arm of a person.
Optionally, the off-Shift determination condition library includes at least two sets of determination conditions, where the determination conditions include at least one set of condition factors, and the set of condition factors include a content feature tag and a set time threshold corresponding to the content feature tag.
Optionally, the identifying a specific content feature tag in the video frame includes:
and determining a content feature tag contained in the target judgment condition, and identifying the content feature tag contained in the target judgment condition as the specific content feature tag in the video picture.
The invention has the beneficial effects that:
according to the off-duty method and the off-duty system based on video image recognition, provided by the invention, the image acquisition equipment is used for acquiring the video image of the station to be monitored in real time and sending the video image to the image processing equipment in real time; the image processing equipment is used for receiving the video image in real time, identifying the specific content characteristic labels in the video image, and recording the appearance time period and/or disappearance time period of each specific content characteristic label; judging whether the target judgment condition is met or not based on the comparison result between the appearance time period and/or the disappearance time period of each specific content characteristic label and the target judgment condition, if so, judging that the station to be monitored is in the off-duty state, otherwise, determining that the station to be monitored is in the normal on-duty state; the target judgment condition is obtained by determining the judgment condition corresponding to the identification information of the station to be monitored according to the corresponding relation between the station identification and the judgment condition. The off-duty judgment condition library based on the setting can realize the long-term stable supervision of any number of posts and any types of posts, achieve the effect of achieving twice the result with half the effort, reduce the occurrence of production accidents caused by off-duty events and ensure the safety production of enterprises.
Drawings
Fig. 1 is a schematic flow chart of a method for identifying off Shift based on a video image according to a first embodiment of the present invention;
FIG. 2 is a flowchart illustrating another off-Shift identification method based on video images according to a first embodiment of the present invention;
FIG. 3 is a schematic diagram of a content feature tag record according to a first embodiment of the present invention;
fig. 4 is a schematic structural diagram of a system for recognizing off Shift based on video images according to a second embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following detailed description and accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The first embodiment is as follows:
in order to solve the problem how to implement long-term stable monitoring on any number of posts and any type of posts, reduce the occurrence of off-post events, reduce production accidents caused by the off-post events and ensure the safe production of enterprises, the embodiment provides an off-post identification method based on video images.
Referring to fig. 1-2, the off Shift identification method based on video image mainly includes the following steps:
s101, collecting a video image of a station to be monitored in real time.
The video image acquisition can be carried out through image acquisition equipment such as a monitoring camera arranged at a fixed position, the installation of the image acquisition equipment can ensure that images of people working at related stations are clearly shot, and the pictures to be acquired can be different aiming at different types of stations, for example, aiming at a station A, only the situation that the people are on the station, namely the images of the people, is required to be ensured, and the installation of the corresponding image acquisition equipment can be relatively random; if the requirement on the staff on post is relatively high for the post B, the face of the staff needs to be ensured to be in a specific position, and the installation position needs to be ensured at the moment, so that the face of the staff can be shot by the image acquisition equipment under the condition that the staff normally works and does not leave the post.
And S102, identifying a specific content feature label in the video picture.
Optionally, the specific content feature tag includes at least one of a whole body, a face, and an arm of the person.
Aiming at different types of posts, the post requirements are possibly different, and on the premise of ensuring that whether the post is normally on can be accurately identified, the content feature label in the video picture can be selectively identified, so that the processing load of the image processing equipment is reduced, and the operation efficiency is improved.
Optionally, the content feature tag included in the target determination condition is determined, and the content feature tag included in the target determination condition is used as the specific content feature tag to identify the specific content feature tag in the video picture. For the content feature tags not included in the target determination condition, there is no need to perform identification, and there is no need to record the appearance time period and disappearance time period thereof.
The target judgment condition is obtained based on the station to be monitored, and the judgment condition corresponding to the identification information of the station to be monitored is obtained from the off-duty judgment condition library according to the corresponding relation between the station identification and the judgment condition.
Different stations may have different on-duty requirements, and the determination condition most matched with the station identification is selected according to the corresponding relation between the station identification and the determination condition, so that the monitoring requirements can be met to the greatest extent, and the monitoring accuracy is improved.
Optionally, the identification information of the workstation to be monitored includes at least one of a name of the workstation and a serial number of the workstation.
In this embodiment, the off-Shift determination condition library includes at least two sets of determination conditions, where one determination condition includes at least one set of condition factors, and one set of condition factors includes a content feature tag and a corresponding set time threshold.
For example, 10 sets of determination conditions are set in the off-shift determination conditions, and are respectively set for different types of stations of an enterprise, for example, the first set of determination conditions includes a set of condition factors, the set of condition factors includes a content feature label "face", and a set time threshold corresponding to the content feature label is "1 minute", that is, in a video picture, if the disappearance time of the "face" picture is accumulated to 1 minute, it is determined that the station is off-shift; for another example, the second set of condition factors includes content feature labels "whole body" and "arm", where the set time threshold corresponding to "whole body" is "2 minutes", and the set time threshold corresponding to "arm" is "1 minute", that is, when the time during which "whole body" does not appear in the video picture continues to reach more than 2 minutes, or the time during which "arm" disappears continues to reach more than 1 minute, it is determined that the post is off duty. For other sets of judgment conditions, the setting can be flexibly set according to the actual requirements of the posts, and the detailed description is omitted.
By setting the off-Shift judgment condition library, aiming at different types of posts of an enterprise, the method can adopt a targeted off-Shift identification mode simultaneously, carry out accurate off-Shift monitoring and improve the management efficiency.
In other embodiments of the present invention, the specific content feature tag in the video image is identified, and the post area of the workstation to be monitored needs to be identified, and the content feature tag outside the post area is not considered, and only the content feature tag inside the post area is processed. The problem that workers are in a video picture but do not work on a working post and cannot be identified is solved. The setting of the post area needs to be flexibly set according to the requirement of the post on the activity range of the staff.
And S103, recording the appearance time period and/or the disappearance time period of each specific content feature tag.
Referring to fig. 3, the specific content feature tags, which are assumed to include three content feature tags of "whole body", "arm", and "face", record, as shown in fig. 3, an appearance period and a disappearance period of each specific content feature tag for the video image of the workstation to be monitored, where the appearance period is indicated by a solid line, the disappearance period is indicated by a dotted line, and a start time and an end time of each period are marked in the period.
S104, judging whether the target judgment condition is met or not based on the comparison result between the appearance time period and/or the disappearance time period of each specific content feature tag and the target judgment condition; if yes, go to step S105; if not, go to step S106.
For example, station pipeline A:
target determination conditions:
condition factor 1:
human number value: 5; time: 1 minute;
off Shift scene: and if the number of people in the picture is not 5 and lasts for more than 1 minute, judging that the target judgment condition is met, and judging that the current post to be monitored is off duty.
For another example, station manual assembly station B:
target determination conditions:
condition factor 1: the front side of the human face; time: 10 seconds;
condition factor 2: number of arms: 2; time: 10 seconds;
off Shift scene: if the duration time exceeds 10 seconds without the face in the picture, or the number of arms is not 2 and the duration time exceeds 10 seconds, the target judgment condition is judged to be met, and off Shift is judged.
And S105, judging that the station to be monitored is in the off-Shift state.
Optionally, when it is determined that the station to be monitored is in the off-post state, an alarm signal is generated to alarm the manager, so that the manager can notify the off-post manager to arrive at the post as soon as possible.
And S106, determining that the station to be monitored is in a normal on-duty state.
The off-duty identification method based on the video images comprises the steps of collecting the video images of stations to be monitored in real time, identifying specific content characteristic labels in video pictures, and recording appearance time periods and/or disappearance time periods of the specific content characteristic labels; judging whether the target judgment condition is met or not based on the comparison result between the appearance time period and/or the disappearance time period of each specific content characteristic label and the target judgment condition, if so, judging that the station to be monitored is in the off-duty state, otherwise, determining that the station to be monitored is in the normal on-duty state; the target judgment condition is obtained by determining the judgment condition corresponding to the identification information of the station to be monitored according to the corresponding relation between the station identification and the judgment condition. The off-duty judgment condition library based on the setting can realize the long-term stable supervision of any number of posts and any types of posts, achieve the effect of achieving twice the result with half the effort, reduce the occurrence of production accidents caused by off-duty events and ensure the safety production of enterprises.
Example two:
in this embodiment, on the basis of the first embodiment, a system for identifying off Shift based on a video image is provided, which is used to implement the method for identifying off Shift based on a video image described in the first embodiment, please refer to fig. 4, and the system mainly includes:
the image acquisition device 41 is in communication connection with the image processing device 42, and the image acquisition device 41 is in communication connection with the image processing device 42, so that data transmission is realized. The communication connection mode includes but is not limited to wired transmission, wireless transmission, wherein the wireless transmission can be WiFi, infrared, Zigbee, bluetooth, 3G/4G, etc.
The image capturing device 41 includes, but is not limited to, a digital camera, a video camera, etc., and the image processing device 42 includes, but is not limited to, a computer, a server, etc.
In this embodiment, the image processing device 42 may be connected to a plurality of image capturing devices 41 at the same time, and may perform off-post detection on the video images corresponding to the stations to be monitored, which are captured by the image capturing devices 41, respectively. On the basis of the off-duty judgment condition library, the target judgment conditions corresponding to the image acquisition equipment 41 are selected, off-duty monitoring can be performed on different types and a large number of stations, off-duty monitoring can be performed on the stations to be monitored more pertinently, and accuracy of monitoring results is improved.
The image acquisition device 41 is used for acquiring a video image of the workstation to be monitored in real time and sending the video image to the image processing device 42 in real time.
The image processing device 42 is configured to receive a video image in real time, identify specific content feature tags in the video image, and record appearance time periods and/or disappearance time periods of the specific content feature tags; judging whether the target judgment condition is met or not based on the comparison result between the appearance time period and/or the disappearance time period of each specific content characteristic label and the target judgment condition, if so, judging that the station to be monitored is in the off-duty state, otherwise, determining that the station to be monitored is in the normal on-duty state; the target determination condition is obtained by acquiring a determination condition corresponding to the identification information of the workstation to be monitored from the off-Shift determination condition by the image processing device 42 according to the correspondence between the workstation identification and the determination condition.
In the present embodiment, the image processing device 42 is configured to determine the content feature tag included in the target determination condition, and identify the content feature tag included in the target determination condition as the specific content feature tag in the video screen.
Optionally, the identification information of the workstation to be monitored includes at least one of a name of the workstation and a serial number of the workstation.
Optionally, the specific content feature tag includes at least one of a whole body, a face, and an arm of the person.
In this embodiment, the off-Shift determination condition library includes at least two sets of determination conditions, where the determination conditions include at least one set of condition factors, and a set of condition factors includes a content feature tag and a corresponding set time threshold.
According to the off-duty system based on video image identification, provided by the invention, the image acquisition equipment is used for acquiring the video image of the station to be monitored in real time and sending the video image to the image processing equipment in real time; the image processing equipment is used for receiving the video image in real time, identifying the specific content characteristic labels in the video image, and recording the appearance time period and/or disappearance time period of each specific content characteristic label; judging whether the target judgment condition is met or not based on the comparison result between the appearance time period and/or the disappearance time period of each specific content characteristic label and the target judgment condition, if so, judging that the station to be monitored is in the off-duty state, otherwise, determining that the station to be monitored is in the normal on-duty state; the target judgment condition is obtained by determining the judgment condition corresponding to the identification information of the station to be monitored according to the corresponding relation between the station identification and the judgment condition. The off-duty judgment condition library based on the setting can realize the long-term stable supervision of any number of posts and any types of posts, achieve the effect of achieving twice the result with half the effort, reduce the occurrence of production accidents caused by off-duty events and ensure the safety production of enterprises.
It will be apparent to those skilled in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be centralized on a single computing device or distributed across a network of computing devices, and optionally they may be implemented in program code executable by a computing device, such that they may be stored on a computer storage medium (ROM/RAM, magnetic disks, optical disks) and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The foregoing is a more detailed description of the present invention that is presented in conjunction with specific embodiments, and the practice of the invention is not to be considered limited to those descriptions. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (10)
1. A method for identifying off Shift based on video images, comprising:
acquiring a video image of a station to be monitored in real time;
identifying a specific content feature tag in a video picture;
recording the appearance time period and/or disappearance time period of each specific content feature tag;
judging whether the target judgment condition is met or not based on a comparison result between the appearance time period and/or disappearance time period of each specific content feature tag and the target judgment condition;
if so, judging that the station to be monitored is in an off-Shift state;
otherwise, determining that the station to be monitored is in a normal on-duty state;
and the target judgment condition is obtained by acquiring the judgment condition corresponding to the identification information of the station to be monitored from the off-post judgment condition library according to the corresponding relation between the station identification and the judgment condition.
2. The method for identifying off Shift according to claim 1, wherein the identification information of the workstation to be monitored comprises at least one of a workstation name and a workstation number.
3. The method for identifying off Shift based on video images of claim 1, wherein the specific content feature tag comprises at least one of a whole body, a face, and an arm of a person.
4. The method for identifying off Shift according to any one of claims 1 to 3, wherein the off Shift decision library includes at least two sets of decision conditions, and the decision conditions include at least one set of condition factors, and the set of condition factors includes a content feature tag and a corresponding set time threshold.
5. The method for identifying off Shift based on video images according to claim 1, wherein said identifying a specific content feature tag in a video frame comprises:
and determining a content feature tag contained in the target judgment condition, and identifying the content feature tag contained in the target judgment condition as the specific content feature tag in the video picture.
6. A video image recognition off-Shift system is characterized by comprising an image acquisition device and an image processing device, wherein the image acquisition device is in communication connection with the image processing device to realize data transmission;
the image acquisition equipment is used for acquiring a video image of a station to be monitored in real time and sending the video image to the image processing equipment in real time;
the image processing equipment is used for receiving the video image in real time, identifying specific content characteristic tags in a video picture, and recording the appearance time period and/or disappearance time period of each specific content characteristic tag; judging whether the target judgment condition is met or not based on the comparison result between the appearance time interval and/or disappearance time interval of each specific content feature label and the target judgment condition, if so, judging that the station to be monitored is in the off-duty state, otherwise, determining that the station to be monitored is in the normal on-duty state; and the target judgment condition is obtained by acquiring the judgment condition corresponding to the identification information of the station to be monitored from the off-Shift judgment condition by the image processing equipment according to the corresponding relation between the station identification and the judgment condition.
7. The video image-based off Shift system of claim 6, wherein the identification information of the workstation to be monitored includes at least one of a workstation name and a workstation number.
8. The video-image-based recognition off Shift system of claim 6, wherein the specific content feature tag comprises at least one of a whole body, a face, and an arm of a person.
9. The video image recognition off Shift system according to any one of claims 6 to 8, wherein the off Shift decision library includes at least two sets of decision conditions, the decision conditions including at least one set of condition factors, the set of condition factors including a content feature tag and a set time threshold corresponding thereto.
10. The video image-based off Shift system according to claim 9, wherein identifying the specific content feature tag in the video frame comprises:
and determining a content feature tag contained in the target judgment condition, and identifying the content feature tag contained in the target judgment condition as the specific content feature tag in the video picture.
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| CN111369703A (en) * | 2020-03-20 | 2020-07-03 | 北京金山云网络技术有限公司 | Online time determination method, device, server and medium |
| CN113052049A (en) * | 2021-03-18 | 2021-06-29 | 国网内蒙古东部电力有限公司 | Off-duty detection method and device based on artificial intelligence tool identification |
| CN113067845A (en) * | 2020-12-06 | 2021-07-02 | 泰州市朗嘉馨网络科技有限公司 | On-duty detection platform applying numerical analysis |
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