Intelligent counting system based on video image monitoring

Abstract

The application discloses an intelligent counting system based on video image monitoring, which comprises a server, data storage, input object reference dynamic data capture comparison, an image counter, self-checking whether a fault occurs, a display showing state information, highly suspected object input suspected object storage and artificial recognition judgment, wherein the server stores data through a video collected by a front-end video collector, the server inputs data through an input module and transmits the data of the input module to the data storage, and the data storage is subdivided into a sub-storage A interval, a sub-storage B interval and a sub-storage C interval.The application has the advantages that the server, the data storage, the input object reference dynamic data capture comparison, the highly suspected object input suspected object storage and the artificial recognition judgment can improve data, accurately determine highly suspected objects, have high accuracy, capture accurately, run with the server, have high running speed and high capture efficiency.

Description

Technical Field

[0001] This invention relates to the field of intelligent counting technology for video image monitoring, and more specifically to an intelligent counting system based on video image monitoring. Background Technology

[0002] The intelligent counting system based on video image monitoring is a system that can improve data, accurately identify highly suspected objects, has a high accuracy rate, and can capture objects accurately. It runs on a server, has a fast operating speed, and high capture efficiency.

[0003] Existing video image monitoring methods are inconvenient to capture, cannot provide complete data, and are inaccurate in identifying highly suspected objects. For example, CN114286073A discloses a video image online detection system and method based on graphic encoding, which includes a video transmitter, a video receiver, and an FSMC bus. The video transmitter includes a front-end video image decoder, a front-end FPGA, a front-end video image encoder, an electro-optical conversion module, a front-end ARM, a clock valid counter, a pixel counter, a line counter, and a frame rate counter. The video receiver includes a photoelectric conversion module, a back-end video image decoder, a back-end FPGA, a back-end video image encoder, a back-end ARM, a clock valid counter, a pixel counter, a line counter, and a frame rate counter. This invention uses unified and simple graphic encoding to address common video fault types in the video image link, enabling rapid online fault location when abnormal video image display occurs. The fault location is accurate and fast, requires no power outage, and does not increase hardware costs.

[0004] Therefore, an intelligent counting system based on video image monitoring is invented to solve the problems of inconvenient capture, incomplete data, and inaccurate identification of highly suspected objects. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent counting system based on video image monitoring to solve the problems of inconvenient capture, incomplete data, and inaccurate identification of highly suspected objects in the technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent counting system based on video image monitoring, comprising a server, data storage, input object reference dynamic data capture and comparison, an image counter, a self-check for fault occurrence, a display showing status information, highly suspected object input and suspected object storage, and manual identification and judgment. The server stores data using video captured by a front-end video camera. The server inputs data through an input module and transmits the data from the input module to the data storage. The data storage is subdivided into sub-storage A, sub-storage B, and sub-storage C. Sub-storage B stores video, capturing dynamic video objects through video content. Sub-storage A of the data storage is used to input captured objects. The captured input objects in sub-storage A are further refined into highly suspected erroneous reference objects and correct reference objects. These refined highly suspected erroneous reference objects and correct reference objects are provided to the input object reference dynamic data capture and comparison. The input object reference dynamic data capture and comparison is counted using an image counter. After the counter counts, a dynamic flow self-test is performed. This dynamic flow self-test detects whether the video is paused and whether a fault has occurred. If a fault is detected, the system disconnects and stops working, displaying status information on the monitor. If a fault is detected, the system closes and operates normally, displaying status information on the monitor. The sub-storage C area of ​​the data storage is used to capture suspected objects. Objects that cannot be captured by the dynamic data capture comparison are entered into the suspected object storage. The captured suspected object storage is then compared with the dynamic data capture again. By comparing highly suspected incorrect reference objects and correct reference objects again, if the comparison is successful, the image counter will count. If the comparison is still highly suspected, the highly suspected object will be entered into the suspected object storage. This solves the problems of inconvenient capture, incomplete data, and inaccurate identification of highly suspected objects.

[0007] Preferably, the server is used to process objects that capture video content, and the server runs faster.

[0008] Preferably, the data storage is subdivided into sub-storage A intervals, which are used to store highly suspected erroneous reference objects and correct reference objects. Sub-storage A intervals are the partitioned storage spaces for data storage.

[0009] Preferably, the data storage is subdivided into sub-storage B intervals, which are used to store videos. Memory exceeding the sub-storage B intervals can easily overwrite the previous memory. Sub-storage B intervals are the partitioned storage spaces for data storage.

[0010] Preferably, the data storage is subdivided into sub-storage C intervals, which are used to store temporary suspected objects. Once a suspected object is identified, it is transferred separately, and the data is automatically deleted after transfer. Sub-storage C intervals are the divided storage spaces for data storage.

[0011] Preferably, the highly suspected object input suspected object storage is judged by manual identification, and the accuracy of manual identification is relatively high.

[0012] Preferably, when the manual identification and judgment is correct, the object will be added to the storage of the correct reference object, that is, the correct reference object of the sub-storage A interval, and the storage space will be divided.

[0013] Preferably, when the manual identification and judgment is negative, the object to be added to the storage of highly suspected erroneous reference objects will be entered, which is the highly suspected erroneous reference object in sub-storage A, and the storage space will be divided.

[0014] Preferably, the self-check to determine if a fault has occurred is equivalent to normal playback of the video, which is used for self-checking and ensures the normal operation of the system.

[0015] Preferably, the display status information is used to display system information status, that is, to facilitate the display of information content, and the display status information is used to display system information.

[0016] The technical effects and advantages provided by the present invention in the above technical solution are as follows: The technical effects are that the server, data storage, input object reference dynamic data capture and comparison, highly suspected object input and suspected object storage, and manual identification and judgment are all included; The advantages are that it can improve the data, accurately judge highly suspected objects, has a high accuracy rate, capture accuracy, and runs on a server with fast running speed and high capture efficiency. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall system of the server of the present invention.

[0018] Explanation of reference numerals in the attached figures:

[0019] Server, front-end video capture, input module, data storage, sub-storage A section, capture input object storage, highly suspected erroneous reference object, correct reference object, sub-storage B section, capture dynamic video object storage, input object reference dynamic data capture comparison, image counter, dynamic flow self-test, self-test for fault occurrence, yes, disconnect, stop working, no, close, stop working, monitor displays status information, sub-storage C section, capture suspected object storage, suspected object input storage, secondary input object reference dynamic data capture comparison, highly suspected object input suspected object storage, manual identification and judgment, input supplement to correct reference object storage, input supplement to highly suspected erroneous reference object storage. Detailed Implementation

[0020] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0021] This invention provides, for example Figure 1The system, illustrated, is an intelligent counting system based on video image monitoring. It includes a server, data storage, input object reference dynamic data capture and comparison, an image counter, a self-check function for faults, a display showing status information, a storage of highly suspected objects, and manual identification and judgment. The server stores video data captured by the front-end video system. Data is input through an input module and then transmitted to the data storage. The data storage is subdivided into three sub-storage areas: A, B, and C. Sub-storage area B stores video, capturing dynamic video objects. Sub-storage area A is used for input capture objects. The captured input objects in sub-storage area A are further refined into highly suspected erroneous reference objects and correct reference objects. These refined reference objects are provided to the input object reference dynamic data capture and comparison system, which is then processed by the image counter. After the image counter counts, a dynamic flow self-test is performed. This test detects video pauses and faults. If a fault is detected, the system disconnects and stops working, displaying status information on the monitor. If no fault is detected, the system closes and resumes normal operation, displaying status information on the monitor. The data storage is further subdivided into sub-storage C areas for capturing suspected objects. Objects that cannot be captured by the dynamic data capture comparison are entered into the suspected object storage. These suspected objects are then captured again, and the dynamic data capture comparison is repeated. This process involves comparing highly suspected incorrect reference objects with correct reference objects. If the comparison is successful, the image counter counts again. If the second comparison still identifies a highly suspected reference object, it is entered into the highly suspected object storage.

[0022] The server is used to process objects captured from video content. Data storage is subdivided into sub-storage area A, which stores highly suspected erroneous reference objects and correct reference objects. Data storage is further subdivided into sub-storage area B, which stores video. Memory exceeding sub-storage area B is used to overwrite previous memory. Data storage is also subdivided into sub-storage area C, which stores temporary suspected objects. Once a suspected object is identified, it is transmitted separately, and the data is automatically deleted after transmission. Highly suspected objects are entered into the suspected object storage and manually identified. If the manual identification is positive, the object is entered into the correct reference object storage, which is the correct reference object in sub-storage area A. If the manual identification is negative, the object is entered into the highly suspected erroneous reference object storage, which is the highly suspected erroneous reference object in sub-storage area A. The self-check function checks for faults, ensuring normal video playback. The monitor displays status information to show the system status, providing information for easy display.

[0023] Working principle of this invention:

[0024] Refer to the instruction manual appendix Figure 1When using this device, the captured video from the front-end video capture unit is transmitted to the server. The server processes the video to capture reference objects. The server has a fast processing speed, and the data storage is subdivided into three sub-storage areas: Sub-storage Area A, Sub-storage Area B, and Sub-storage Area C. Sub-storage Area B stores the video, capturing dynamic video objects through video content. Sub-storage Area A is used for input capture objects. The captured input objects in Sub-storage Area A are further refined into highly suspected erroneous reference objects and correct reference objects. These refined highly suspected erroneous reference objects and correct reference objects are provided to the input object for reference dynamic data capture comparison. The input object reference dynamic data capture comparison is counted by an image counter. After the image counter counts, a dynamic flow self-check is performed. The dynamic flow self-check is used to detect whether the video is interrupted and to check for faults. If a fault is detected, the system disconnects and stops working, and status information is displayed on the monitor. If a fault is detected, the system closes and operates normally, and status information is displayed on the monitor. Sub-storage Area C, subdivided into data storage areas, is used for capturing suspected objects. The process involves several steps: First, a suspected object is entered into the dynamic data capture and comparison system. If the captured object cannot be identified, it is considered a suspected object. This suspected object is then entered into the image counter. If the captured suspected object is still a highly suspected object, it is entered into the highly suspected object storage. This highly suspected object is then manually identified and judged. If the manual judgment is positive, it is added to the storage of the correct reference object, which is the correct reference object in sub-storage A. If the manual judgment is negative, it is added to the storage of the highly suspected erroneous reference object, which is also the highly suspected erroneous reference object in sub-storage A. The display status information is used to show the system information status, facilitating the presentation of information content.

Claims

1. An intelligent counting system based on video image monitoring, comprising a server, data storage, input object reference dynamic data capture and comparison, an image counter, a self-check for fault occurrence, a display showing status information, input of highly suspected objects, storage of suspected objects, and manual identification and judgment, characterized in that: The server stores video data captured by the front-end video camera. The server inputs data through an input module and transmits the input data to the data storage. The data storage is subdivided into three sub-storage areas: Sub-storage Area A, Sub-storage Area B, and Sub-storage Area C. Sub-storage Area B stores video, capturing dynamic video objects based on the video content. Sub-storage Area A is used to input capture objects. The captured input objects in Sub-storage Area A are further refined into highly suspected error reference objects and correct reference objects. These refined highly suspected error reference objects and correct reference objects are provided to the input object for reference dynamic data capture comparison. The input object reference dynamic data capture comparison is counted using an image counter. After counting, a dynamic flow self-check is performed. The dynamic flow self-check detects whether the video is paused and checks for faults. If a fault is detected, the system disconnects and stops working, displaying status information on the monitor. If a fault is detected, the system stops working. The system is closed, operating normally, and displaying status information on the monitor. The sub-storage C section of the data storage is used to capture suspected objects. The suspected objects that cannot be captured by the input object reference dynamic data capture comparison are input storage. The suspected object input storage is input into the suspected object capture storage. The captured suspected object storage is then compared with the input object reference dynamic data again. By comparing the highly suspected incorrect reference object and the correct reference object again, if the second input object reference dynamic data capture comparison is successful, it enters the image counter for counting. If the second input object reference dynamic data capture comparison is still a highly suspected reference object, it enters the highly suspected object input suspected object storage. If the manual identification judgment is yes, it will be input to the correct reference object storage, which is the correct reference object in sub-storage A section. If the manual identification judgment is no, it will be input to the highly suspected incorrect reference object storage, which is the highly suspected incorrect reference object in sub-storage A section.

2. The intelligent counting system based on video image monitoring according to claim 1, characterized in that: The server is used to process objects that capture video content.

3. The intelligent counting system based on video image monitoring according to claim 1, characterized in that: The data storage is subdivided into sub-storage area A, which is used to store highly suspected erroneous reference objects and correct reference objects.

4. The intelligent counting system based on video image monitoring according to claim 1, characterized in that: The data storage is subdivided into sub-storage B sections, which are used to store videos. Memory exceeding the sub-storage B section can be used to overwrite previous memory.

5. The intelligent counting system based on video image monitoring according to claim 1, characterized in that: The data storage is subdivided into sub-storage C intervals, which are used to store temporary suspected objects. Once a suspected object is identified, it is transmitted separately, and the data is automatically deleted after transmission.

6. The intelligent counting system based on video image monitoring according to claim 1, characterized in that: The highly suspected objects are entered into the suspected object storage and judged manually.

7. The intelligent counting system based on video image monitoring according to claim 1, characterized in that: The self-test to check for faults refers to the normal playback of the video, and is used for self-testing.

8. The intelligent counting system based on video image monitoring according to claim 1, characterized in that: The display status information is used to show the system information status, that is, the information content is easy to display.

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