Information processing device and information processing method

Abstract

To provide a technology for reducing effort required to prepare relearning data.SOLUTION: In an information processing device (a server device 10), a first acquisition unit 20 acquires multiple captured images obtained by capturing the outside of a vehicle, a detection unit 22 detects, from the acquired multiple captured images, multiple images related to unsafe driving using a learned model, and an image extraction unit 24 extracts, from the detected multiple images related to unsafe driving, an image to be a candidate for relearning data of the learned model.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an information processing device and an information processing method for detecting an image relating to dangerous driving. [Background technology]

[0002] Patent Document 1 discloses a driving support device that allows drivers to easily check analysis results, thereby contributing to safe driving. This device extracts the top 50 mesh areas where violation data frequently occurs, and the violation data that corresponds to the mesh areas surrounding each mesh area from a central storage unit, and determines the top 20 candidate areas with the most cases that fall within the candidate area as areas where violations frequently occur, and displays and expands them on the analysis screen. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP2007-141212 Summary of the Invention [Problem to be solved by the invention]

[0004] In a technology that uses an image recognition model to detect images related to dangerous driving from images captured outside the vehicle, it is desirable to retrain the model to improve detection accuracy, but preparing data for retraining requires a great deal of effort.

[0005] An object of the present invention is to provide a technique that can reduce the effort required to prepare re-learning data. [Means for solving the problem]

[0006] In order to solve the above problems, an information processing device of one embodiment of the present invention includes an acquisition unit that acquires multiple captured images of the outside of the vehicle, a detection unit that detects multiple images related to dangerous driving from the multiple acquired captured images using a trained model, and an image extraction unit that extracts images that are candidates for re-learning data for the trained model from the multiple detected images related to dangerous driving. The image extraction unit extracts an image when the certainty of an image related to detected dangerous driving is less than a threshold, or when the certainty of the image is equal to or greater than a threshold and the image satisfies specified conditions that make it prone to erroneous detection.

[0007] Another aspect of the present invention is an information processing method executed by a computer, the information processing method including the steps of acquiring a plurality of captured images of an area outside a vehicle, detecting a plurality of images related to dangerous driving from the acquired plurality of captured images using a trained model, and extracting images that are candidates for re-learning data for the trained model from the detected plurality of images related to dangerous driving. Image Extraction Steps. In the image extraction step, the image is extracted when the certainty of the image related to the detected dangerous driving is less than a threshold, or when the certainty of the image is equal to or greater than the threshold and satisfies specified conditions that make the image prone to erroneous detection. Effect of the Invention

[0008] According to the present invention, it is possible to reduce the effort required to prepare re-learning data. [Brief description of the drawings]

[0009] [Figure 1] 1 is a diagram showing a configuration of a driving evaluation system according to an embodiment; [Diagram 2] FIG. 13 is a flowchart illustrating a process for extracting candidate images for re-learning data. [Diagram 3] 2 is a diagram showing an example of a plurality of images displayed on the user terminal of FIG. 1; [Figure 4] 1. FIG. 4 is a diagram showing another example of an image displayed on the user terminal of FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] 1 shows a configuration of a driving evaluation system 1 according to an embodiment. The driving evaluation system 1 analyzes captured images stored in a drive recorder of a vehicle to detect images related to dangerous driving of the vehicle. Images related to dangerous driving can also be called images of dangerous scenes.

[0011] The driving evaluation system 1 includes a server device 10, a first terminal 12, a second terminal 14, and a user terminal 16. The first terminal 12, the second terminal 14, and the user terminal 16 are each connected to a network (not shown) by wired communication or wireless communication, and communicate with the server device 10 via the network.

[0012] The server device 10 is installed in, for example, a data center, and functions as an information processing device. The first terminal 12 is used, for example, by an employee at an automobile dealership. The second terminal 14 is used by an employee who checks images related to detected dangerous driving. The user terminal 16 is owned by the driver of the vehicle or the driver's family. The driving evaluation system 1 may include a plurality of first terminals 12, a plurality of second terminals 14, and a plurality of user terminals 16.

[0013] For example, when a vehicle is brought to a car dealer, an operator retrieves a series of captured images from the drive recorder of the vehicle and inputs the captured images to the server device 10 via the first terminal 12 of the car dealer. The server device 10 analyzes the input series of captured images to detect images of the vehicle performing dangerous driving, and calculates a driving score based on the detection result. The detection of images related to dangerous driving is performed using a first trained model that is machine-learned based on learning data. The detected images related to dangerous driving are provided to the driver's family or the like by the user terminal 16, so that the family or the like can easily understand what kind of dangerous driving the driver performed. The calculated driving score is provided to the driver of the vehicle via the first terminal 12 or the user terminal 16, and is provided to the driver's family or the like via the user terminal 16, so that the driver and his / her family can know the driver's driving ability.

[0014] The server device 10 extracts images (hereinafter also referred to as candidate images) that are candidates for re-learning data for the first trained model from the multiple detected images related to dangerous driving, and supplies them to the second terminal 14. The second terminal 14 displays the extracted candidate images. An operator visually checks whether the displayed images are images related to dangerous driving, and inputs the confirmation result to the second terminal 14. The confirmation of the images by the operator and the input of the confirmation result are called annotation, and the operator is called an annotator. By extracting images that are candidates for re-learning data from the multiple detected images related to dangerous driving, it is possible to reduce the time that the annotator spends checking images for annotation for re-learning. Therefore, it is possible to reduce the effort required to prepare re-learning data.

[0015] The server device 10 uses the annotated images as re-learning data to re-learn the first trained model, thereby improving the accuracy of detecting images related to dangerous driving.

[0016] The image storage unit 11 stores captured images of the vehicle periphery captured by an on-board camera. The captured images stored in the image storage unit 11 are arranged in chronological order and are moving images. The first terminal 12 acquires the moving images stored in the drive recorder from the image storage unit 11 and transmits the acquired moving images to the server device 10. The image storage unit 11 is hardware such as a flash memory, and may be manually removed from the vehicle by an operator and connected to the first terminal 12. The image storage unit 11 may be connected to the server device 10 via a network such as the Internet using a communication device of the vehicle.

[0017] The second terminal 14 is a personal computer or the like used by the annotator, which displays candidate images for the re-learning data and accepts input of the results of confirmation of the images by the annotator.

[0018] The user terminal 16 is, for example, a smartphone, a tablet, or a personal computer owned by the driver, the driver's family, etc. The user terminal 16 outputs information related to dangerous driving provided by the server device 10.

[0019] The server device 10 includes a first acquisition unit 20, a detection unit 22, an image extraction unit 24, a first reception unit 26, a first learning unit 28, a second acquisition unit 30, a second reception unit 32, an estimation unit 34, a second learning unit 36, a video extraction unit 38, a video provision unit 40, an image provision unit 42, and a driving score calculation unit 44.

[0020] The configuration of the server device 10 can be realized in hardware terms by the CPU, memory, and other LSIs of any computer, and in software terms by programs loaded into memory, but here we depict functional blocks realized by the cooperation of these. Therefore, it will be understood by those skilled in the art that these functional blocks can be realized in various forms by hardware alone, software alone, or a combination of both.

[0021] The first acquisition unit 20 acquires from the image storage unit 11 a plurality of captured images of the outside of the vehicle captured by the vehicle-mounted camera.

[0022] The detection unit 22 detects images related to dangerous driving using the first learned model from the multiple captured images acquired by the first acquisition unit 20. Examples of images related to dangerous driving include an image of not stopping at a stop position, an image of ignoring traffic signals, an image of going out of a lane, an image of dangerous right or left turns, an image of obstructing pedestrians, an image of speeding, an image of a bicycle not slowing down when suddenly darting out, etc. The detection unit 22 may detect one image from a series of multiple captured images that show a certain dangerous driving.

[0023] The detection unit 22 executes object detection processing for all captured images using the first trained model. The first trained model includes a publicly known object detection model. The captured images, for example, constitute a video of 30 frames per second and are arranged in chronological order. The detection unit 22 derives the position coordinates of the detected object on the captured image, the type of the object, and a confidence level. The position coordinates of the object may be the position coordinates of the four corners of a rectangle surrounding the object. The confidence level is also called a confidence value or reliability, and indicates the likelihood of the detected object. The detection unit 22 detects images related to dangerous driving based on the type and position of the detected object.

[0024] The detection unit 22 detects a marking indicating a stop position from the captured image and identifies the stop position. The marking indicating a stop position is a specific traffic marking such as a stop line or a stop sign.

[0025] The detection unit 22 detects predetermined objects, such as vending machines, in the captured images as feature points, and calculates the vehicle speed by tracking the feature points included in the captured images arranged in chronological order.

[0026] The detection unit 22 detects that the vehicle did not stop at the stop position based on the identified stop position and the calculated vehicle speed. When a failure to stop is detected, the detection unit 22 detects an image showing a sign indicating a stop position as an image related to dangerous driving. The detection unit 22 attaches a label "stop sign violation" as a type of dangerous driving to the detected image related to dangerous driving, and also attaches a confidence level related to the sign indicating the stop position.

[0027] The detection unit 22 detects a red light from the captured image, identifies a stop position, and detects that the vehicle did not stop before reaching the stop position based on the identified stop position and the calculated vehicle speed. When a failure to stop is detected, the detection unit 22 detects an image showing a red light as an image related to dangerous driving. The detection unit 22 attaches a label of "ignoring a traffic light" as a type of dangerous driving to the detected image related to dangerous driving, and also attaches a confidence level related to the red light.

[0028] The detection unit 22 detects a crosswalk and pedestrians from the captured image to identify a stop position, and detects that the vehicle did not stop at the stop position based on the identified stop position and the calculated vehicle speed. When a failure to stop is detected, the detection unit 22 detects an image showing a crosswalk and pedestrians as an image related to dangerous driving. The detection unit 22 attaches a label of "pedestrian obstruction" as a type of dangerous driving to the detected image related to dangerous driving, and also attaches a confidence level related to the crosswalk and pedestrians.

[0029] The detection unit 22 detects the speed limit from the traffic signs in the captured image, and detects that the vehicle is exceeding the speed limit based on the detected speed limit and the calculated vehicle speed. When speeding is detected, the detection unit 22 detects an image showing a traffic sign indicating the speed limit as an image related to dangerous driving. The detection unit 22 attaches a label of "speeding" to the detected image related to dangerous driving as a type of dangerous driving, and also attaches a confidence level related to the traffic sign.

[0030] The image extraction unit 24 extracts images that are candidates for re-learning data for the first trained model from the multiple images related to dangerous driving detected by the detection unit 22. The image extraction unit 24 extracts an image when the certainty of the detected image related to dangerous driving is less than a threshold, or when the certainty of the image is equal to or greater than a threshold and the image satisfies a predetermined condition that makes it easy for a false detection to occur. The image extraction unit 24 does not extract an image when the certainty of the detected image related to dangerous driving is equal to or greater than a threshold and the image does not satisfy a predetermined condition. The threshold can be determined as appropriate by experiments or simulations.

[0031] The predetermined conditions are that the image is taken at night, that the image is taken in the rain, or that the image reflects the windshield. The image extraction unit 24 determines whether the image is taken at night based on the brightness of the image. The image extraction unit 24 detects the presence or absence of raindrops on the windshield in the image, and determines that the image is an image taken in the rain if the image reflects raindrops. Raindrops can be detected, for example, using an object detection model. The image extraction unit 24 derives the degree of reflection of the windshield in the image based on the brightness of the image, and determines that the image is an image reflecting the windshield if the derived degree of reflection is equal to or greater than a predetermined value.

[0032] The image extraction unit 24 supplies the second terminal 14 with a plurality of captured images in chronological order including the extracted candidate image as video data. The video data includes a plurality of captured images from a captured image captured before the candidate image to a captured image captured after the candidate image, for example, including captured images from several seconds before to several seconds after the candidate image. The image extraction unit 24 supplies the video data for each extracted candidate image to the second terminal 14. The second terminal 14 displays the plurality of candidate images supplied from the image extraction unit 24 together with characters indicating the type of dangerous driving. The second terminal 14 can play a video including the candidate image in response to an operation by the annotator. The annotator looks at the candidate image and the video, checks whether each displayed candidate image is an image related to dangerous driving, and inputs the check result to the second terminal 14. If the annotator can determine whether the candidate image is an image related to dangerous driving from only the candidate image, it does not need to play the video. Note that the image extraction unit 24 may supply the extracted candidate image to the second terminal 14, but not supply the video data.

[0033] For example, in an image related to dangerous driving where the type of dangerous driving is "violation of stop signs," if there is no stop line or stop sign, some object in the image may be erroneously detected as a sign indicating a stop position. The annotator determines that this image is not related to dangerous driving.

[0034] The annotator inputs a confirmation result indicating that a candidate image that has been confirmed to be an image related to dangerous driving is an image related to dangerous driving, while the annotator inputs a confirmation result indicating that a candidate image that has been confirmed to be an image related to dangerous driving is an image related to dangerous driving, for which the annotator has confirmed that the candidate image is not an image related to dangerous driving.

[0035] The annotator may specify an image that represents a dangerous situation among a plurality of captured images from an image captured before to an image captured after a candidate image confirmed to be an image related to dangerous driving, and may input a confirmation result indicating that the specified image is a learning image for a model for extracting thumbnail images, which will be described later, for the specified image. A dangerous situation is, for example, a situation in which a vehicle approaches from the side in an intersection in the case of a stop violation or ignoring a traffic light, a situation in which a pedestrian is obstructed, a situation in which the vehicle is likely to come into contact with a pedestrian in front of the vehicle, and other situations that are likely to interest the driver's family and others. The image specified by the annotator may be different from the detected image related to dangerous driving. This is because the detected image related to dangerous driving does not necessarily best represent a dangerous situation.

[0036] The second terminal 14 attaches the input confirmation result to information for identifying the candidate image or the image representing a dangerous situation, and transmits the information to the server device 10.

[0037] The first reception unit 26 of the server device 10 receives the identification information of the image transmitted from the second terminal 14, supplies the identification information of the candidate image to the first learning unit 28, and supplies the identification information of the image representing a dangerous situation to the second learning unit 36. The processing of the second learning unit 36 ​​will be described later.

[0038] The first learning unit 28 uses the candidate image extracted because the confidence level is less than the threshold as re-learning data, and re-learns the first trained model. The first learning unit 28 re-learns using the label according to the confirmation result based on the identification information of the candidate image supplied from the first receiving unit 26 and the attached confirmation result.

[0039] The first learning unit 28 re-learns, using the object types already assigned as labels, about the candidate images for which confirmation results indicating that the images are related to dangerous driving have been input. By re-learning, it is possible to further increase the confidence level of images that have been correctly determined to be related to dangerous driving.

[0040] The first learning unit 28 re-learns, using the object type such as "unknown" as a label, for the candidate image for which a confirmation result indicating that the image is not related to dangerous driving has been input. Re-learning makes it difficult to erroneously determine that an image in which dangerous driving is not occurring is an image related to dangerous driving.

[0041] The first learning unit 28 uses, as re-learning data, candidate images that are confirmed not to be images related to dangerous driving among candidate images extracted because the confidence level is equal to or greater than a threshold and because the candidate images satisfy a predetermined condition, and re-learns the first trained model. This makes it difficult to erroneously determine that an image in which dangerous driving is not being performed is an image related to dangerous driving in a situation where erroneous detection is likely to occur.

[0042] The re-learning may be performed periodically at a predetermined frequency, or when a predetermined number of images of re-learning data have been collected. By continuously re-learning the model, the accuracy of detecting images related to dangerous driving can be improved.

[0043] The first learning unit 28 does not use, as re-learning data, candidate images that are confirmed to be images related to dangerous driving among candidate images extracted by having a certainty level equal to or higher than a threshold and satisfying a predetermined condition. Even if re-learning is performed using such correctly detected images, the certainty level is unlikely to be further increased because the certainty level is equal to or higher than a threshold. By not using such images for re-learning, the efficiency of re-learning can be improved.

[0044] In addition, for a candidate image that has been confirmed to be an image related to dangerous driving, if the type of dangerous driving is incorrect, the annotator may also input the correct type of dangerous driving as a confirmation result. In this case, the first learning unit 28 re-learns the candidate image for which the confirmation result indicating the correct type of dangerous driving has been input, using the type of object corresponding to the correct type of dangerous driving as a label. By re-learning, it becomes easier to determine the correct type for an image related to dangerous driving that has been erroneously determined to be a dangerous driving image.

[0045] 2 shows a flowchart of the process of extracting candidate images for re-learning data. The first acquisition unit 20 acquires a series of captured images captured by an in-vehicle camera from the image storage unit 11 (S10). The detection unit 22 detects images related to dangerous driving from the acquired series of captured images (S12).

[0046] If the certainty of the detected image is not equal to or greater than the threshold (N in S14), the image extraction unit 24 extracts the detected image as a candidate image for re-learning data (S22) and ends the process. If the certainty of the detected image is equal to or greater than the threshold (Y in S14), and the detected image is a nighttime image (Y in S16), the process proceeds to S22. If the detected image is not a nighttime image (N in S16), and the detected image is a rainy image (Y in S18), the process proceeds to S22. If the detected image is not a rainy image (N in S18), and the detected image is an image that reflects the windshield (Y in S20), the process proceeds to S22. If the detected image is not an image that reflects the windshield (N in S20), the process ends.

[0047] Next, a description will be given of a process of providing a video including images relating to dangerous driving to the user terminal 16. The first acquisition unit 20 also supplies the acquired multiple captured images in chronological order to the video extraction unit .

[0048] The video extraction unit 38 extracts, from the multiple captured images in chronological order acquired by the first acquisition unit 20, multiple captured images ranging from a captured image captured before the image related to dangerous driving detected by the detection unit 22 to a captured image captured after the image, in a time interval corresponding to the type of dangerous driving. The time interval is a period from a first hour before the image related to dangerous driving is captured to a second hour after the image related to dangerous driving is captured. The multiple extracted captured images constitute a video.

[0049] If the type of dangerous driving is "violation of stop sign", the first time is shorter than the second time. For example, the first time may be 5 seconds and the second time may be 10 seconds. The image related to dangerous driving is an image near the position where the vehicle should stop, and it is easy to understand the situation after passing the position where the vehicle should stop. For example, it is possible to understand the driver's driving, such as continuing on without stopping even when another vehicle appears from the side after passing the position where the vehicle should stop.

[0050] If the type of dangerous driving is "ignoring traffic signals," the first time is longer than the second time. For example, the first time may be 10 seconds and the second time may be 5 seconds. This makes it easier to understand the driver's driving and judgment in front of the traffic signal.

[0051] If the type of dangerous driving is "obstructing a pedestrian," the first time is longer than the second time. For example, the first time may be 10 seconds and the second time may be 5 seconds. This makes it easier to understand the driver's driving and judgment before approaching a pedestrian.

[0052] The specific values ​​of the first time and the second time can be appropriately determined by experiments and simulations. In the case of stormy weather such as rain, the moving image extraction unit 38 may extend the time period by multiplying the time period for cases other than stormy weather by a coefficient greater than 1.

[0053] If the type of dangerous driving is "speeding", the time section is a period during which speeding occurs regardless of weather.

[0054] A video composed of the extracted multiple captured images can be played back on the user terminal 16 as described below, and can be checked by the driver's family members, etc. Since appropriate playback sections can be cut out for each type of dangerous driving, the family members, etc. can easily understand driving behavior that needs to be improved.

[0055] The first acquisition unit 20 also supplies the acquired multiple captured images in chronological order to the estimation unit 34. The estimation unit 34 estimates an image of high interest to the user from the multiple captured images in chronological order including an image related to dangerous driving detected by the detection unit 22, using the second trained model. The estimation unit 34 estimates one image of high interest to the user for one image related to dangerous driving. The estimated image is associated with the image related to dangerous driving. The second trained model is trained using images that represent dangerous situations such as a situation in which another vehicle approaches from the side in an intersection.

[0056] The image providing unit 42 provides the image estimated by the estimation unit 34 to the user terminal 16. The user terminal 16 displays the provided image as a thumbnail image. The user terminal 16 can display a list of multiple images. The driver's family members and the like can view thumbnail images that are likely to interest them.

[0057] Fig. 3 shows an example of a plurality of images displayed on the user terminal 16 of Fig. 1. The user terminal 16 displays each image with characters indicating the type of dangerous driving superimposed thereon. When the user performs an operation on the user terminal 16 to select one of the images, the user terminal 16 transmits identification information of the selected image to the server device 10.

[0058] The second receiving unit 32 of the server device 10 receives the image identification information transmitted from the user terminal 16, and supplies it to the video providing unit .

[0059] The video providing unit 40 provides the user terminal 16 with a plurality of captured images extracted by the video extracting unit 38, including an image corresponding to the image identification information transmitted from the user terminal 16, as video data. The user terminal 16 plays the provided video. The driver's family members, etc., can view the image relating to dangerous driving and the video before and after that image by performing an operation to select a thumbnail image.

[0060] When a user performs an operation on user terminal 16 to pause the playback during playback of a video, user terminal 16 pauses the playback and transmits to server device 10 identification information of the image at the time of the pause.

[0061] The second acquisition unit 30 acquires the identification information of the image transmitted from the user terminal 16 at the time when the playback was paused, and supplies this to the second learning unit .

[0062] The second learning unit 36 ​​retrains the second trained model using the image identified by the identification information supplied from the second acquisition unit 30 and the image representing a dangerous situation identified by the identification information supplied from the first reception unit 26 as retraining data. The second learning unit 36 ​​may use only images that have been paused a predetermined number of times or more as retraining data. Retraining may be performed periodically at a predetermined frequency, or may be performed when retraining data for a predetermined number of images has been collected. This allows the second trained model to be retrained so as to be able to estimate an image similar to an image that an unspecified user has been interested in and stopped playing, or an image similar to an image representing a dangerous situation confirmed by an annotator.

[0063] FIG. 4 shows another example of an image displayed on the user terminal 16 of FIG. 1. This image is also a thumbnail image provided by the server device 10, and the type of dangerous driving is "sudden sudden jump of a bicycle." The image providing unit 42 performs highlight processing to brighten the bicycle, which is a dangerous object 60 in the image. The image providing unit 42 also overlays an emphasis image 62 on the image to make the dangerous object 60 stand out. The emphasis image 62 is a frame of a conspicuous color that surrounds the bicycle. The image providing unit 42 may attach a comment 64 of driving advice predetermined for each type of dangerous driving to the image. The image providing unit 42 may change the content of the driving advice depending on the weather.

[0064] The driving score calculation unit 44 calculates a driving score representing the driving skill of the driver based on the detection result by the detection unit 22. The driving score is calculated based on the number of times an image related to dangerous driving is detected, etc., so that the higher the driving score is, the higher the driving skill is, and the lower the driving skill is, so that the lower the driving skill is.

[0065] The driving score calculated by the driving score calculation unit 44 is transmitted to the user terminal 16. The user terminal 16 displays the received driving score to the driver and his / her family by voice and image. The driving score may be transmitted to the first terminal 12. The first terminal 12 displays the received driving score to the driver by voice and image. In this way, the driving evaluation system 1 can diagnose the driver's skill from only captured images, so that diagnosis can be easily made at automobile dealerships, etc. Therefore, when elderly people, etc. bring their vehicles to automobile dealerships, it becomes possible to diagnose their driving skills and provide advice.

[0066] The present invention has been described above based on the embodiments. The embodiments are merely examples, and it will be understood by those skilled in the art that various modifications are possible in the combination of each component and each processing step, and that such modifications are also within the scope of the present invention.

[0067] For example, in the embodiment, images related to dangerous driving are detected only from captured images, but the detection unit 22 may also detect images related to dangerous driving using detection values ​​from various in-vehicle sensors. For example, the detection unit 22 may detect images of a vehicle not stopping at a position where it should stop, ignoring traffic signals, etc., based on the vehicle speed detected by a vehicle speed sensor. The detection unit 22 may obtain the speed limit based on vehicle position information and map information. [Explanation of symbols]

[0068] 1 driving evaluation system, 10 server device, 16 user terminal, 20 first acquisition unit, 22 detection unit, 24 image extraction unit, 28 first learning unit, 34 estimation unit, 38 video extraction unit, 40 video provision unit, 42 image provision unit.

Claims

1. An acquisition unit that acquires a plurality of captured images of the outside of the vehicle; A detection unit that detects a plurality of images related to dangerous driving from the plurality of captured images obtained by using a trained model; an image extraction unit that extracts images that are candidates for re-learning data for the trained model from a plurality of images related to the detected dangerous driving; Equipped with The image extraction unit extracts an image when a certainty factor of an image related to the detected dangerous driving is less than a threshold, and when a certainty factor of the image is equal to or greater than a threshold and the image satisfies a predetermined condition that makes it likely to be erroneously detected.

23. An information processing apparatus comprising:

2. A learning unit uses, as re-learning data, images that are confirmed not to be images related to dangerous driving among the images extracted because the confidence level is equal to or greater than a threshold and the predetermined condition is satisfied, and re-learns the trained model.

2. The information processing apparatus according to claim 1,

3. The detection unit detects an image related to dangerous driving from a plurality of captured images in chronological order acquired from the vehicle by the acquisition unit, The information processing device includes: a video extraction unit that extracts, from the plurality of captured images acquired in chronological order, a plurality of captured images ranging from a captured image captured before an image related to the detected dangerous driving to a captured image captured after the image, the plurality of captured images being in a time section corresponding to a type of the dangerous driving; a moving image providing unit that provides the extracted plurality of captured images to a user terminal as moving image data; 3. The information processing apparatus according to claim 1, further comprising:

4. The detection unit detects an image related to dangerous driving from a plurality of captured images in chronological order acquired from the vehicle by the acquisition unit, The information processing device includes: an estimation unit that estimates an image of high interest to a user from a plurality of captured images in chronological order including an image related to the detected dangerous driving, using a trained model; an image providing unit that provides the estimated image to a user terminal; 4. The information processing apparatus according to claim 1, further comprising:

5. 1. A computer-implemented information processing method, comprising: acquiring a plurality of captured images of the outside of the vehicle; A step of detecting a plurality of images related to dangerous driving from the plurality of captured images obtained using a trained model; An image extraction step of extracting images that are candidates for re-learning data for the trained model from a plurality of images related to the detected dangerous driving; Equipped with In the image extraction step, the image is extracted when the certainty of the detected image related to dangerous driving is less than a threshold, or when the certainty of the image is equal to or greater than the threshold and satisfies a predetermined condition that makes the image likely to be erroneously detected.

23. An information processing method comprising:

Images