Data processing device, data processing method, and data processing program

Abstract

To provide a data processing device, a data processing method, and a program capable of identifying a plurality of types of vehicles from an image.SOLUTION: The data-processing device includes an acquiring unit 292 that acquires a video of a moving image to be analyzed captured by a monitoring camera or the like, a vehicle-extraction processing unit 294 that performs processing of extracting vehicles by making an HTTP request to an external server including a pre-learned image-recognition model that performs image-recognition such as YOLO, and a vehicle-identification processing unit 296 that performs processing of identifying types of vehicles by generating a prompt for requesting identification of types of vehicles extracted by the vehicle-extraction processing unit 294 and inputting the prompt to a first generative AI model such as Gemini (Internet retrieval <URL:https: / / gemini.google.com / ?hl=ja>).SELECTED DRAWING: Figure 5

Description

Technical Field

[0001] The technology of the present disclosure relates to a data processing device, a data processing method, and a data processing program.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] By the way, when conducting a road traffic volume survey, it is often the case that a person sits beside the road to check and count the types of vehicles passing through the road and then compile the count results into a report, but it is necessary for a person to go beside the road. Therefore, in recent years, efforts have been made to utilize surveillance images such as surveillance cameras and have AI identify vehicles from the video for automation.

[0005] However, when AI (Artificial Intelligence) identifies vehicles from video, it may be necessary to classify vehicles into multiple types (for example, 8 vehicle types). In this case, since a large amount of data for each vehicle type is required, there are problems in identification, and it may be difficult to grasp a specific vehicle type.

Means for Solving the Problems

[0006] A first aspect of the technology of this disclosure is a data processing device that includes: an acquisition unit that acquires video footage of a vehicle; a vehicle extraction processing unit that uses a pre-trained image recognition model to extract the vehicle and its vehicle registration plate from the video footage acquired by the acquisition unit; and a vehicle identification processing unit that uses a pre-trained first generation AI model to identify the type of vehicle from the extraction results of the vehicle extraction processing unit.

[0007] A second aspect of the technology of this disclosure is a data processing method in which a computer acquires video footage of a vehicle, extracts the vehicle and its license plate from the acquired video footage using a pre-trained image recognition model, and performs a process to identify the type of vehicle from the extraction results using a pre-trained first generation AI model.

[0008] A third aspect of the technology of this disclosure is a data processing program that causes a computer to acquire video footage of a vehicle, extract the vehicle and its license plate from the acquired video footage using a pre-trained image recognition model, and perform a process to identify the type of vehicle from the extraction results using a pre-trained first generation AI model. [Brief explanation of the drawing]

[0009] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and user terminal. [Figure 3] This shows a portion of the outline of the specific processing. [Figure 4] This shows the rest of the outline of the specific process. [Figure 5] The functional configuration of a specific processing unit of a data processing device is shown in general terms. [Figure 6] This diagram outlines an example of the operation flow of a specific process performed by a data processing device. [Figure 7]An example of a graph summarizing traffic volume information by vehicle type is shown. [Figure 8] An example of a map reflecting traffic volume survey results is shown. [Figure 9] A schematic diagram of the functional configuration of a modified specific processing unit of a data processing device is shown. [Figure 10] This is a conceptual diagram showing a modified example of the main functions of a data processing device. [Modes for carrying out the invention]

[0010] Hereinafter, an example of an embodiment of the data processing device, data processing method, and program relating to the technology of this disclosure will be described with reference to the attached drawings.

[0011] First, let's explain the terminology used in the following explanation.

[0012] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), or an APU (Accelerated Processing Unit).

[0013] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0014] In the following embodiments, the tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0015] In the following embodiments, the tagged communication I / F (Interface) is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0016] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0017] FIG. 1 shows an example of the configuration of the data processing system 10 according to the embodiment.

[0018] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a user terminal 14. Examples of the data processing device 12 include a server. Examples of the user terminal 14 include a personal computer or a smartphone. In this embodiment, the data processing device 12 is an example of the "data processing device" according to the technology of the present disclosure.

[0019] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of the "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network), etc.

[0020] The user terminal 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0021] The reception device 38 includes a keyboard, a mouse, etc., and receives user input. Also, the reception device 38 may receive user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator with a touch panel, or may receive user input by voice by detecting the user's voice with a microphone. The control unit 46A transmits data indicating the received user input to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires data indicating the user input.

[0022] The output device 40 includes a display 40A, a speaker 40B, etc., and presents data to a person by outputting the data in a form perceptible to the person (e.g., voice and / or text). The display 40A displays visible information such as text and images according to an instruction from the processor 46. The speaker 40B outputs voice according to an instruction from the processor 46.

[0023] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0024] Figure 2 shows an example of the main functions of the data processing device 12 and the user terminal 14.

[0025] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0026] At the user terminal 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 62. The reception output program 62 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 62 from the storage 50 and executes the read reception output program 62 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 62 executed on the RAM 48.

[0027] Next, we will explain the processing of the specific processing unit 290 when the data processing device 12 performs specific processing to investigate road traffic volume from the video.

[0028] In this embodiment, the identification processing unit 290 utilizes video footage from surveillance cameras such as CCTV (closed-circuit television) installed on roads to extract vehicles from the video footage, identify the vehicle type, and record the data. Specifically, as shown in Figure 3, video footage captured by surveillance cameras is input to the data processing unit 12. The data processing unit 12 uses image recognition to extract vehicles and vehicle registration plates (hereinafter referred to as license plates) from the input video footage, and identifies the type of vehicle from the extracted vehicle image and license plate. The example in Figure 3 shows an example where a vehicle with a Nagoya license plate is extracted and recognized as a commercial vehicle. The identification results are then recorded as data.

[0029] Furthermore, as shown in Figure 4, the specific processing unit 290 performs the process of creating a traffic volume survey report using the digitized and recorded information. For example, it creates a report summarizing traffic volume information for each type of vehicle. Then, by performing a digitization process such as formatting the created report, the traffic volume survey results are superimposed and displayed on a map or the like.

[0030] In detail, as shown in Figure 5, the specific processing unit 290 includes the functions of an acquisition unit 292, a vehicle extraction processing unit 294, a vehicle identification processing unit 296, a report creation processing unit 298, and a digitization processing unit 300.

[0031] The acquisition unit 292 acquires video footage of the video subject to analysis, captured by a surveillance camera or the like, from the user terminal 14. Alternatively, the acquisition unit 292 may acquire video footage of the video subject to analysis directly from the surveillance camera or the like.

[0032] The vehicle extraction processing unit 294 performs the process of extracting vehicles present in the video. In this embodiment, the vehicle extraction process is performed by making an HTTP request to an external server equipped with a pre-trained image recognition model such as YOLO (You Only Look Once) for image recognition.

[0033] The vehicle identification processing unit 296 performs the process of identifying the type of vehicle extracted. In this embodiment, the process of identifying the type of vehicle is performed by making an HTTP request to an external server equipped with a pre-trained first generation AI (Artificial Intelligence) model. For example, the vehicle extraction processing unit 294 generates a prompt requesting identification of the type of vehicle extracted, and Gemini (Internet search <url: https: gemini.google.com ?hl="ja">The system identifies the type of vehicle by inputting prompts to the first generated AI model, such as the image shown. The results of the vehicle type identification are then stored in a database 302 constructed in storage 32, etc. Specifically, in this embodiment, the vehicle extraction processing unit 294 identifies the type of vehicle by inputting a prompt to Gemini along with the extracted image, requesting identification of which of the eight types it belongs to: light passenger car, passenger car, bus, light commercial vehicle, small commercial vehicle, passenger and cargo vehicle, regular commercial vehicle, and special vehicle.

[0034] The report creation processing unit 298 performs the process of creating a traffic volume survey results report based on the information stored in the database 302. In this embodiment, the process of creating a traffic volume survey results report is performed using the information stored in the database 302 via an HTTP request to an external server equipped with a pre-trained second generation AI model. For example, a prompt is generated to request the creation of a traffic volume survey results report using the information stored in the database 302, and ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> The system generates a report by inputting prompts to the second generation AI model, such as ). The generated report is then output to the user terminal 14 and displayed.

[0035] The digitization processing unit 300 digitizes the report created by the report creation processing unit 298 by performing format conversion and other operations, and then performs processing such as overlaying the traffic volume survey results for each road onto a map. In this embodiment, the process of digitizing the report and displaying it on the map is performed by making an HTTP request to an external server equipped with a third generation AI model. Specifically, a prompt is generated to request the processing of digitizing the created report by format conversion and overlaying the traffic volume survey results for each road onto a map, and by inputting the prompt to the third generation AI model such as GPTs (customized proprietary ChatGPT), the report is digitized and a map with the traffic volume survey results for each road overlaid is generated. As a result, a map reflecting the survey results for each road can be viewed on a network 54 such as the internet.

[0036] In this embodiment, prompts are automatically generated in each processing unit, but they may also be generated manually.

[0037] Furthermore, each generative AI model is input with a prompt containing instructions, and at least one of the following inference data: audio data representing speech, text data representing text, and image data representing images. Each generative AI model infers from the input inference data according to the instructions indicated by the prompt, and outputs the inference results in data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0038] Next, the operation of the data processing system 10 will be explained.

[0039] An example of the flow of a specific processing method will be explained with reference to Figure 6. Note that the flow of a specific processing method shown in Figure 6 is an example of a "data processing method" related to the technology disclosed herein.

[0040] In step S100, the acquisition unit 292 acquires the video footage to be analyzed and proceeds to step S102. For example, the user operates the user terminal 14 and sends the image to be analyzed to the data processing device 12, causing the acquisition unit 292 to acquire the video footage from the user terminal 14.

[0041] In step S102, the vehicle extraction processing unit 294 performs the process of extracting vehicles present in the video and proceeds to step S104. For example, by making an HTTP request to an external server equipped with an image recognition model such as YOLO, the system extracts vehicles from the video being analyzed and obtains the extraction results from the external server. The vehicle extraction results are then stored in the database 302.

[0042] In step S104, the vehicle identification processing unit 296 performs the process of identifying the type of vehicle and proceeds to step S106. For example, it generates a prompt requesting identification of the type of vehicle extracted by the vehicle extraction processing unit 294, and inputs this prompt to a generating AI such as Gemini to identify the type of vehicle and obtain the identification result. The vehicle identification result is then stored in the database 302.

[0043] In step S106, the report creation processing unit 298 creates a traffic volume survey report based on the information stored in the database 302 and proceeds to step S108. For example, it generates a prompt requesting the creation of a traffic volume survey report using the information stored in the database 302, and creates the report by inputting it to a generation AI such as ChatGPT, and obtains the creation result. For example, as shown in Figure 7, it creates a report summarizing traffic volume information for each type of vehicle. The created report is then stored in the database 302. Figure 7 shows an example of a graph summarizing traffic volume information for each type of vehicle.

[0044] In step S108, the digitization processing unit 300 performs digitization processing and proceeds to step S110. The digitization processing digitizes the report created by the report creation processing unit 298 by converting the report format, and displays the traffic volume survey results for each road superimposed on the map. For example, a prompt is generated to request that the created report be format-converted and digitized, and that the traffic volume survey results for each road be superimposed on the map. This prompt is then input to a generation AI such as GPTs to generate a map with the traffic volume survey results for each road superimposed. The generated map is then output to a network 54 such as the internet, allowing users to view the map reflecting the survey results. For example, as shown in Figure 8, the map may display messages such as "Traffic signal adjustments are needed at intersections where congestion occurs" as a point of congestion, "At 8 a.m., there is a high volume of regular cargo vehicles and congestion is high" as peak traffic volume, or "Due to the high volume of private car traffic, the use of public transportation should be promoted" as traffic volume by usage.

[0045] In step S110, the acquisition unit 292 determines whether or not there is another video. If the determination is affirmative, the process returns to step S100 and the above process is repeated, and the series of specific processes ends when the determination is negative.

[0046] By performing this identification process, it is possible to extract and identify vehicles from video footage. Furthermore, traffic volume survey reports can be easily created using the vehicle identification results.

[0047] Furthermore, since data identifying the type of vehicle can be provided immediately, it will be possible to contribute to the efficiency of planned road maintenance.

[0048] Furthermore, it can contribute to the collection of road passability information. For example, as shown in Figure 9, the specific processing unit 290 may be further equipped with a creation processing unit 304 that creates passability information based on the acquired video and the identification results of the vehicle identification processing unit 296. Alternatively, the specific processing unit 290 may be equipped with an acquisition unit 292, a vehicle extraction processing unit 294, a vehicle identification processing unit 296, and a creation processing unit 304. The creation processing unit 304 extracts vehicles that were able to pass from the video among the vehicles identified by the vehicle identification processing unit 296 and identifies the type of vehicle, thereby creating passability information for each type of vehicle. This makes it possible to determine the passability of each vehicle type from road video in the event of a disaster and provide passability information immediately.

[0049] In the above embodiment, an example was described in which the vehicle extraction processing unit 294, the vehicle identification processing unit 296, the report creation processing unit 298, and the digitization processing unit 300 each perform their respective processing by making an HTTP request to an external server, but the invention is not limited to this. For example, as shown in Figure 10, the image recognition model 58A, the first generation AI model 58B, the second generation AI model 58C, and the third generation AI model 58D may be stored in the storage 32 of the data processing device 12, and each may be used by the specific processing unit 290. Alternatively, at least one of the image recognition model 58A, the first generation AI model 58B, the second generation AI model 58C, and the third generation AI model 58D may be stored in the storage 32, and the other models may be processed by an external server via an HTTP request. Furthermore, although the first generation AI model 58B, the second generation AI model 58C, and the third generation AI model 58D were described as separate models, they may be implemented as a single generation AI model, or a model integrating at least two or more models may be used.

[0050] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0051] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0052] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0053] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0054] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0055] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0056] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0057] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0058] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0059] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0060] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0061] The following additional information is disclosed regarding the embodiments described above. <Note 1> An acquisition unit that acquires video footage of the vehicle, A vehicle extraction processing unit extracts the vehicle and the vehicle's registration plate from the video acquired by the acquisition unit using a pre-trained image recognition model, A vehicle identification processing unit identifies the type of vehicle from the extraction results of the vehicle extraction processing unit using a pre-trained first generation AI model, A data processing device that includes a data processing device.

[0062] <Note 2> The data processing device described in Appendix 1 further includes a report generation processing unit that generates a road traffic volume report based on the vehicle type identification result using a pre-trained second generation AI model.

[0063] <Note 3> The data processing device according to Appendix 2 further includes a digitization processing unit that digitizes the report using a pre-trained third generative AI model.

[0064] <Note 4> A data processing device according to any one of the appendices 1 to 3, further comprising a creation processing unit that creates road accessibility information based on the aforementioned video and the identification results of the vehicle identification processing unit.

[0065] <Note 5> Computers The vehicle captures video footage, Using a pre-trained image recognition model, the vehicle and its license plate are extracted from the acquired video. A data processing method that uses a pre-trained first generative AI model to perform a process of identifying the type of vehicle from the extraction results.

[0066] <Note 6> On the computer, The vehicle captures video footage, Using a pre-trained image recognition model, the vehicle and its license plate are extracted from the acquired video. A data processing program that uses a pre-trained first-generation AI model to perform the process of identifying the type of vehicle from the extracted results. [Explanation of symbols]

[0067] 10 Data Processing Systems 12 Data Processing Devices 14 User terminals 56 Specific Processing Program 56A Image Recognition Model 56B First Generative AI Model 56C Second Generation AI Model 56D Third Generation AI Model 290 Specific Processing Unit 292 Acquisition Department 294 Vehicle Extraction Processing Unit 296 Vehicle Identification Processing Unit 298 Report Creation Processing Unit 300 Digitalization Processing Unit 302 Databases 304 Creation Processing Unit< / url:>

Claims

1. An acquisition unit that acquires video footage of the vehicle, A vehicle extraction processing unit extracts the vehicle and the vehicle's registration plate from the video acquired by the acquisition unit using a pre-trained image recognition model, The vehicle extraction processing unit generates a prompt requesting identification of the type of vehicle extracted by the vehicle extraction processing unit, and the vehicle identification processing unit uses a pre-trained first generation AI model to identify the type of vehicle from the image data of the vehicle extracted by the vehicle extraction processing unit and the text data of the vehicle registration plate. A report creation processing unit that creates a road traffic volume report based on the identification result of the type of vehicle, A digitization processing unit generates a prompt requesting the aforementioned report to be formatted and digitized, and to be processed to overlay the traffic volume survey results for each road onto a map, and by inputting this prompt to a pre-trained third generation AI model, generates a map in which the traffic volume survey results for each road are overlaid. A data processing device that includes a data processing device.

2. The data processing device according to claim 1, further comprising a creation processing unit that creates road accessibility information based on the video and the identification results of the vehicle identification processing unit.

3. Computers The vehicle captures video footage, Using a pre-trained image recognition model, the vehicle and its license plate are extracted from the acquired video. A prompt is generated to request identification of the type of vehicle extracted, and the type of vehicle is identified from the extracted vehicle image data and the text data of the vehicle registration plate using a pre-trained first generation AI model. Based on the identification results of the vehicle type, a road traffic volume report is prepared. A data processing method that generates a prompt requesting the aforementioned report to be formatted and digitized, and to be superimposed on a map the traffic volume survey results for each road, and then inputs this prompt to a pre-trained third generation AI model to generate a map with the traffic volume survey results for each road superimposed.

4. On the computer, The vehicle captures video footage, Using a pre-trained image recognition model, the vehicle and its license plate are extracted from the acquired video. A prompt is generated to request identification of the type of vehicle extracted, and the type of vehicle is identified from the extracted vehicle image data and the text data of the vehicle registration plate using a pre-trained first generation AI model. Based on the identification results of the vehicle type, a road traffic volume report is prepared. A data processing program that generates a prompt requesting the aforementioned report to be formatted and digitized, and to be superimposed on a map the traffic volume survey results for each road, and then inputs this prompt to a pre-trained third generation AI model to execute the process of generating a map with the traffic volume survey results for each road superimposed.

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