Video analysis system

Abstract

[Problem] To objectively evaluate online communications such as meetings and lectures, which have become mainstream, in order to carry out communication more efficiently. [Solution] A system according to the present disclosure is a video analysis system that analyzes the reactions of participants during an online session having multiple users on the basis of video acquired by imaging the participants, regardless of whether the participants are displayed on screen, said video analysis system comprising: a video acquisition unit that acquires video obtained by imaging participants in the online session; an analysis unit that analyzes changes in biological reactions of the participants on the basis of the video acquired by the video acquisition unit; a classification unit that classifies the online session type; and a score calculation unit that refers to the analysis result by the analysis unit for each online session classification type and calculates a predetermined score.

Description

[Technical Field]

[0001] The present invention relates to a video analysis system that analyzes the reactions of participants in an environment where an online session is held with multiple participants, based on video images obtained by photographing the participants, regardless of whether the participants are displayed on the screen during the online session. [Background technology]

[0002] A technique for analyzing the emotions felt by others in response to a speaker's utterance is known (see, for example, Patent Document 1). A technique for analyzing changes in a subject's facial expression over a long period of time and estimating the emotions felt during that time is also known (see, for example, Patent Document 2). A technique for identifying the factors that most influenced changes in emotions is also known (see, for example, Patent Documents 3 to 5). A technique for comparing a subject's usual facial expression with their current facial expression and issuing an alert if the facial expression is gloomy is also known (see, for example, Patent Document 6). A technique for comparing a subject's normal (expressionless) facial expression with their current facial expression to determine the subject's level of emotion is also known (see, for example, Patent Documents 7 to 9). Furthermore, a technique for analyzing organizational emotions and the atmosphere felt by individuals within a group is also known (see, for example, Patent Documents 10 and 11). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2019-58625 [Patent Document 2] Japanese Patent Application Laid-Open No. 2016-149063 [Patent Document 3] Japanese Patent Application Publication No. 2020-86559 [Patent Document 4] Japanese Patent Application Laid-Open No. 2000-76421 [Patent Document 5] Japanese Patent Application Publication No. 2017-201499 [Patent Document 6] Japanese Patent Application Publication No. 2018-112831 [Patent Document 7] Japanese Patent Application Laid-Open No. 2011-154665 [Patent Document 8] Japanese Patent Application Laid-Open No. 2012-8949 [Patent Document 9] JP 2013-300 A [Patent Document 10] Japanese Patent Application Laid-Open No. 2011-186521 [Patent Document 11] WO15 / 174426 publication Summary of the Invention [Problem to be solved by the invention]

[0004] All of the above technologies are merely secondary functions in situations where communication in the physical world is the primary focus. In other words, they were not created in response to the recent digital transformation of work and the global pandemic, in which communication for work, classes, etc. is primarily conducted online.

[0005] The present invention aims to objectively evaluate communication in situations where online communication is the norm, such as conferences and lectures, in order to achieve more efficient communication. [Means for solving the problem]

[0006] According to the present invention, A video analysis system that analyzes the reactions of participants in an environment where an online session is held by multiple people, based on video images obtained by photographing the participants, regardless of whether the participants are displayed on a screen during the online session, comprising: a video acquisition unit that acquires video images obtained by photographing the participants during the online session; an analysis unit that analyzes changes in biological reactions of the participant based on the moving images acquired by the moving image acquisition unit; a classification unit that classifies the type of the online session; a score calculation unit that calculates a predetermined score for each classification of the online session by referring to the analysis result by the analysis unit; A video analysis system comprising: [Effects of the Invention]

[0007] According to the present disclosure, by analyzing and evaluating the video footage of a video session, evaluation can be made objectively, particularly regarding the content.

[0008] In particular, according to the present invention, in a situation where online communication is the norm, it is possible to objectively evaluate the communication that has been exchanged in order to carry out more efficient communication. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a diagram showing an overall system according to an embodiment of the present invention; [Figure 2] FIG. 2 is a functional block diagram of an evaluation terminal according to an embodiment of the present invention; [Figure 3] FIG. 2 is a diagram illustrating a first example of a functional configuration of an evaluation terminal according to an embodiment of the present invention. [Figure 4] FIG. 2 is a diagram illustrating a second example of a functional configuration of an evaluation terminal according to an embodiment of the present invention. [Figure 5] FIG. 10 is a diagram illustrating a third example of a functional configuration of the evaluation terminal according to the embodiment of the present invention. [Figure 6] 7 is a screen display example according to the functional configuration example 3 of FIG. 6. [Figure 7] 7 is another example of a screen display according to the functional configuration example 3 of FIG. 6. [Figure 8] FIG. 10 is a diagram illustrating another configuration of functional configuration example 3 of the evaluation terminal according to the embodiment of the present invention. [Figure 9] FIG. 10 is a diagram illustrating another configuration of functional configuration example 3 of the evaluation terminal according to the embodiment of the present invention. [Figure 10]FIG. 1 is a functional block diagram of a system according to an embodiment of the present invention. [Figure 11] FIG. 1 is a diagram illustrating an image of how a system is used in an embodiment of the present invention. [Figure 12] FIG. 1 illustrates elements of a system according to an embodiment of the present invention. [Figure 13] FIG. 1 illustrates elements of a system according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0010] The present disclosure will be described below with reference to the following embodiments. [Item 1] A video analysis system that analyzes the reactions of participants in an environment where an online session is held by multiple people, based on video images obtained by photographing the participants, regardless of whether the participants are displayed on a screen during the online session, comprising: a video acquisition unit that acquires video images obtained by photographing the participants during the online session; an analysis unit that analyzes changes in biological reactions of the participant based on the moving images acquired by the moving image acquisition unit; a classification unit that classifies the type of the online session; a score calculation unit that calculates a predetermined score for each classification of the online session by referring to the analysis result by the analysis unit; Equipped with Video image analysis system. [Item 2] Item 1. The video analysis system according to item 1, the classification unit refers to a face image of a pre-registered employee and at least classifies the session into an internal online session including the employee and an external online session not including the employee; the score calculation unit calculates the analysis as a statistical value for each of the plurality of internal online sessions and the plurality of external online sessions; Video image analysis system. [Item 3] A video analysis device having the configuration of the video analysis system described in item 1. [Item 4] A video analysis program that causes a video analysis device to function as the configuration of the video analysis system described in item 1. [Item 5] A video analysis method that executes the configuration of the video analysis system described in item 1 as steps.

[0011] Preferred embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. In this specification and drawings, components having substantially the same functional configurations are designated by the same reference numerals, and redundant description will be omitted.

[0012] <Basic functions> The video session evaluation system of this embodiment is a system that analyzes and evaluates the unique emotions (e.g., feelings of pleasure or discomfort in response to one's own or another's words or actions, or the degree of such feelings) of a subject of analysis among multiple people in an environment where the multiple people are engaged in a video session (hereinafter, both one-way and two-way sessions are referred to as online sessions) that are different from those of the other people. An online session may be, for example, an online conference, online class, or online chat. Terminals installed in multiple locations are connected to a server via a communication network such as the Internet, allowing video images to be exchanged between the multiple terminals through the server. Video images handled in an online session include facial images and audio of users using the terminals. Video images also include images of documents shared and viewed by multiple users. It is possible to switch between facial images and document images on the screen of each terminal, displaying only one of them, or to divide the display area and display both facial images and document images simultaneously. It is also possible to display an image of one of the multiple people in full screen, or to split the images of some or all of the users into small screens and display them. Of multiple users participating in an online session using terminals, it is possible to designate one or more users as analysis targets. For example, the leader, facilitator, or administrator of the online session (hereinafter collectively referred to as the organizer) designates one of the users as the analysis target. The organizer of an online session is, for example, a lecturer of an online class, a chairperson or facilitator of an online conference, or a coach of a session aimed at coaching. The organizer of an online session is usually one of the multiple users participating in the online session, but may also be a different person who does not participate in the online session. Note that it is also possible to not designate an analysis target, and to treat all participants as analysis targets. Also, the leader, facilitator, or administrator of the online session (hereinafter collectively referred to as the organizer) can designate one of the users as the analysis target. The organizer of an online session is, for example, a lecturer of an online class, a chairperson or facilitator of an online conference, or a coach of a session aimed at coaching.The host of an online session is typically one of multiple users participating in the online session, but may also be a different person who is not participating in the online session.

[0013] In the video session evaluation system according to this embodiment, when a video session is established between multiple terminals, at least a video image acquired from the video session is displayed. The displayed video image is acquired by the terminal, and at least a facial image contained in the video image is identified for each predetermined frame. An evaluation value for the identified facial image is then calculated. The evaluation value is shared as needed. In particular, in this embodiment, the acquired video image is stored in the terminal, analyzed and evaluated on the terminal, and the results are provided to the user of the terminal. Therefore, even if a video session contains personal information or confidential information, for example, the video itself can be analyzed and evaluated without providing the video itself to an external evaluation agency, etc. Furthermore, if necessary, the evaluation results (evaluation values) can be provided to an external terminal, allowing the results to be visualized and cross-analysis, etc. to be performed.

[0014] As shown in Figure 1, the video session evaluation system according to this embodiment includes user terminals 10 and 20 each having at least an input unit such as a camera unit and a microphone unit, a display unit such as a display, and an output unit such as a speaker, a video session service terminal 30 that provides a two-way video session to the user terminals 10 and 20, and an evaluation terminal 40 that performs part of the evaluation of the video session.

[0015] <Hardware configuration example> Each functional block, functional unit, and functional module described below can be configured, for example, using hardware, a DSP (Digital Signal Processor), or software provided in a computer. For example, when configured using software, the software is actually configured with a computer's CPU, RAM, ROM, etc., and is implemented by running a program stored in RAM, ROM, a hard disk, a semiconductor memory, or other storage medium. The series of processes performed by the system and terminal described herein can be implemented using software, hardware, or a combination of software and hardware. A computer program for implementing each function of the information sharing support device 10 according to this embodiment can be created and installed on a PC or the like. A computer-readable storage medium storing such a computer program can also be provided. Examples of storage media include a magnetic disk, an optical disk, a magneto-optical disk, and a flash memory. The computer program may also be distributed, for example, via a network, without using a storage medium.

[0016] The evaluation terminal according to this embodiment acquires moving images from a video session service terminal, identifies at least facial images contained in the moving images for each predetermined frame, and calculates an evaluation value for the facial images (details will be described later).

[0017] <How to get the video> As shown in FIG. 3, the video session service (hereinafter simply referred to as "this service") provided by the video session service terminal enables two-way image and audio communication with user terminals 10 and 20. This service displays video captured by the camera of the other user terminal on the display of the user terminal, and can output audio captured by the microphone of the other user terminal from the speaker. This service is also configured to enable both or either user terminals to record video and audio (collectively referred to as "video, etc.") in the memory of at least one of the user terminals. The recorded video information Vs (hereinafter referred to as "recorded information") is cached in the user terminal that initiated the recording and is recorded only locally on one of the user terminals. If necessary, users can view the recorded information themselves or share it with others within the scope of their use of this service.

[0018] <Functional configuration example 1> Fig. 4 is a block diagram showing an example of the configuration according to this embodiment. As shown in Fig. 4, the video session evaluation system of this embodiment is realized as a functional configuration possessed by a user terminal 10. That is, the user terminal 10 has, as its functions, a video image acquisition unit 11, a biological reaction analysis unit 12, a peculiar determination unit 13, a related event identification unit 14, a clustering unit 15, and an analysis result notification unit 16.

[0019] The video acquisition unit 11 acquires from each terminal video images obtained by capturing images of multiple people (multiple users) using a camera provided in each terminal during an online session. The video images acquired from each terminal may or may not be set to be displayed on the screen of each terminal. In other words, the video acquisition unit 11 acquires video images from each terminal, including video images currently being displayed and video images currently not being displayed on each terminal.

[0020] The biological response analysis unit 12 analyzes changes in biological responses for each of multiple people based on the moving images (whether or not they are being displayed on the screen) acquired by the moving image acquisition unit 11. In this embodiment, the biological response analysis unit 12 separates the moving images acquired by the moving image acquisition unit 11 into a set of images (a collection of frame images) and audio, and analyzes changes in biological responses from each.

[0021] For example, the biological reaction analysis unit 12 analyzes changes in biological reactions related to at least one of facial expression, eye movement, pulse rate, and facial movement by analyzing the user's facial image using frame images separated from the video acquired by the video acquisition unit 11. Furthermore, the biological reaction analysis unit 12 analyzes changes in biological reactions related to at least one of the user's speech content and voice quality by analyzing the audio separated from the video acquired by the video acquisition unit 11.

[0022] When a person's emotions change, this is reflected in changes in biological reactions such as facial expressions, eye movements, pulse rate, facial movements, speech content, and voice quality. In this embodiment, changes in the user's emotions are analyzed by analyzing changes in the user's biological reactions. One example of the emotion analyzed in this embodiment is the degree of comfort / discomfort. In this embodiment, the biological reaction analysis unit 12 quantifies changes in biological reactions according to a predetermined standard, thereby calculating a biological reaction index value that reflects the details of the changes in biological reactions.

[0023] The analysis of facial expression changes is performed, for example, as follows: For each frame image, a facial region is identified within the frame image, and the identified facial expressions are classified into multiple categories according to an image analysis model that has been trained in advance by machine learning. Based on the classification results, the system analyzes whether a positive or negative facial expression change has occurred between consecutive frame images, and the magnitude of the change, and outputs a facial expression change index value according to the analysis results.

[0024] The analysis of changes in gaze is performed, for example, as follows. That is, for each frame image, the eye area is identified within the frame image, and the direction of both eyes is analyzed to analyze where the user is looking. For example, it is analyzed whether the user is looking at the face of the speaker currently being displayed, at the shared material currently being displayed, or looking outside the screen. It may also be possible to analyze whether the gaze movement is large or small, or whether the movement is frequent or infrequent. The gaze change is also related to the user's concentration level. The biological response analysis unit 12 outputs a gaze change index value according to the analysis result of the gaze change.

[0025] The analysis of pulse rate changes is performed, for example, as follows. That is, for each frame image, the facial area is identified within the frame image. Then, using a trained image analysis model that captures the numerical value of facial color information (G in RGB), changes in the G color of the facial surface are analyzed. The results are arranged along the time axis to form a waveform representing changes in color information, and the pulse is identified from this waveform. When a person is nervous, their pulse rate increases, and when they feel calm, their pulse rate slows down. The biological response analysis unit 12 outputs a pulse rate change index value according to the analysis results of the pulse rate changes.

[0026] The analysis of changes in facial movement is performed, for example, as follows. That is, for each frame image, a facial area is identified within the frame image, and the facial direction is analyzed to analyze where the user is looking. For example, it is analyzed whether the user is looking at the face of the currently displayed speaker, the currently displayed shared material, or looking off-screen. It may also be analyzed whether the facial movement is large or small, or whether the movement is frequent or infrequent. It may also be analyzed by combining facial movement and eye movement. For example, it may be analyzed whether the user is looking directly at the currently displayed speaker's face, looking up or down, or looking at an angle. The biological response analysis unit 12 outputs a facial direction change index value according to the analysis result of the change in facial direction.

[0027] The analysis of speech content is performed, for example, as follows. That is, the biological response analysis unit 12 converts speech for a specified period of time (for example, approximately 30 to 150 seconds) into a string of characters by performing known speech recognition processing, and then performs morphological analysis on the string of characters to remove words unnecessary for expressing the conversation, such as particles and articles. The remaining words are then vectorized, and an analysis is performed to determine whether a positive or negative emotional change has occurred, and the extent of the emotional change, and a speech content index value corresponding to the analysis result is output.

[0028] Voice quality analysis is performed, for example, as follows: The biological response analysis unit 12 identifies the acoustic features of the voice by performing known voice analysis processing on the voice for a specified period of time (for example, approximately 30 to 150 seconds). Then, based on the acoustic features, it analyzes whether a positive or negative voice quality change has occurred and to what extent the voice quality change has occurred, and outputs a voice quality change index value according to the analysis result.

[0029] The biological reaction analysis unit 12 calculates a biological reaction index using at least one of the facial expression change index, eye direction change index, pulse rate change index, facial direction change index, speech content index, and voice quality change index calculated as described above. For example, the biological reaction index is calculated by weighting the facial expression change index, eye direction change index, pulse rate change index, facial direction change index, speech content index, and voice quality change index.

[0030] The unique determination unit 13 determines whether or not the change in biological reaction analyzed for the subject of analysis is unique compared to the change in biological reaction analyzed for other people other than the subject of analysis. In this embodiment, the unique determination unit 13 determines whether or not the change in biological reaction analyzed for the subject of analysis is unique compared to other people based on the biological reaction index values ​​calculated for each of the multiple users by the biological reaction analysis unit 12.

[0031] For example, the unique determination unit 13 calculates the variance of the biological reaction index values ​​calculated for each of multiple people by the biological reaction analysis unit 12, and by comparing the biological reaction index value calculated for the person being analyzed with the variance, determines whether the changes in the biological reactions analyzed for the person being analyzed are unique compared to others.

[0032] The following three patterns can be considered when changes in the biological reactions analyzed for the subject are unique compared to others. The first is when no particularly large changes in biological reactions occur in others, but a relatively large change in biological reactions occurs in the subject. The second is when no particularly large changes in biological reactions occur in the subject, but a relatively large change in biological reactions occurs in others. The third is when relatively large changes in biological reactions occur in both the subject and others, but the nature of the change differs between the subject and others.

[0033] The related event identification unit 14 identifies an event occurring with respect to at least one of the subject, other people, and the environment when a change in a biological reaction determined to be unique by the unique determination unit 13 occurs. For example, the related event identification unit 14 identifies, from video images, the behavior and words of the subject when a unique change in a biological reaction occurs in the subject. The related event identification unit 14 also identifies, from video images, the behavior and words of other people when a unique change in a biological reaction occurs in the subject. The related event identification unit 14 also identifies, from video images, the environment when a unique change in a biological reaction occurs in the subject. The environment may be, for example, shared documents displayed on the screen or something that appears in the background of the subject.

[0034] The clustering unit 15 analyzes the degree of correlation between a change in biological reaction determined to be unique by the unique determination unit 13 (for example, one or more combinations of eye contact, pulse rate, facial movement, speech content, and voice quality) and an event occurring when the unique change in biological reaction occurs (an event identified by the related event identification unit 14), and if the correlation is determined to be at a certain level or above, clusters the person or event being analyzed based on the analysis results of the correlation.

[0035] For example, if a specific change in biological reaction corresponds to a negative emotional change and the event occurring when the specific change in biological reaction occurs is also a negative event, a correlation of a certain level or higher is detected. The clustering unit 15 clusters the analysis subject or event into one of multiple pre-segmented classifications according to the content of the event, the degree of negativity, the magnitude of correlation, etc.

[0036] Similarly, if a change in a specific biological reaction corresponds to a positive emotional change and the event occurring when the change in the specific biological reaction occurs is also a positive event, a correlation of a certain level or higher is detected. The clustering unit 15 clusters the analysis subject or event into one of multiple pre-segmented classifications according to the content of the event, the degree of positivity, the magnitude of correlation, etc.

[0037] The analysis result notification unit 16 notifies the person designating the subject of analysis (the subject of analysis or the organizer of the online session) of at least one of the changes in biological reactions determined to be specific by the specific determination unit 13, the events identified by the related event identification unit 14, and the classifications clustered by the clustering unit 15.

[0038] For example, the analysis result notification unit 16 notifies the analysis subject of his / her own words and actions as an event occurring when a unique change in biological reaction occurs in the analysis subject that is different from that of others (one of the three patterns described above; the same applies below). This allows the analysis subject to understand that when he / she behaves in a certain way, he / she feels differently from others. At this time, the analysis subject may also be notified of the unique changes in biological reaction identified for the analysis subject. Furthermore, the analysis subject may also be notified of changes in biological reaction of others to be compared.

[0039] For example, if the emotions felt by others in response to words or actions made by the subject without any particular awareness and with normal emotions, or words or actions made by the subject with a particular awareness and with a certain emotion differ from the emotions felt by the subject himself at the time of the words or actions, the subject will be notified of his or her own words or actions at that time. This makes it possible to discover words or actions that are well-received by others or that are not well-received by others, despite the subject's own awareness.

[0040] Furthermore, the analysis result notification unit 16 notifies the organizer of the online session of events occurring when a unique change in biological reaction occurs in the analysis subject that is different from that of others, along with the unique change in biological reaction. This allows the organizer of the online session to know what events are influencing what emotional changes as phenomena unique to the designated analysis subject. Then, it becomes possible to take appropriate measures for the analysis subject based on the content of the information obtained.

[0041] Furthermore, the analysis result notification unit 16 notifies the organizer of the online session of events occurring when a unique change in the biological reaction of the analysis subject occurs that is different from that of others, or of the clustering results of the analysis subject. This allows the organizer of the online session to understand the behavioral tendencies unique to the analysis subject and predict possible future behaviors and conditions, etc., depending on which classification the specified analysis subject is clustered into. This then makes it possible to take appropriate measures for the analysis subject.

[0042] In the above embodiment, an example has been described in which a biological reaction index value is calculated by quantifying changes in biological reactions according to a predetermined standard, and whether or not the changes in biological reactions analyzed for the subject of analysis are unique compared to others is determined based on the biological reaction index values ​​calculated for each of a plurality of people, but the present invention is not limited to this example. For example, the following may be used.

[0043] That is, the biological reaction analysis unit 12 analyzes the eye movement of each of the multiple people and generates a heat map showing the eye direction. The peculiar determination unit 13 compares the heat map generated by the biological reaction analysis unit 12 for the analysis subject with the heat map generated for other people, and determines whether the change in the biological reaction analyzed for the analysis subject is more peculiar than the change in the biological reaction analyzed for other people.

[0044] As described above, in this embodiment, the video of the video session is stored in the local storage of the user terminal 10, and the above-described analysis is performed on the user terminal 10. Although it may depend on the machine specifications of the user terminal 10, it is possible to analyze the video information without providing it to an external party.

[0045] <Functional configuration example 2> As shown in FIG. 5, the video session evaluation system of this embodiment may include, as functional components, a moving image acquisition unit 11, a biological reaction analysis unit 12, and a reaction information presentation unit 13a.

[0046] The reaction information presenting unit 13a presents information indicating changes in biological reactions analyzed by the biological reaction analyzing unit 12a, including participants not displayed on the screen. For example, the reaction information presenting unit 13a presents information indicating changes in biological reactions to a leader, facilitator, or manager of the online session (hereinafter collectively referred to as the organizer). The organizer of the online session may be, for example, a lecturer of an online class, a chairperson or facilitator of an online conference, or a coach of a session for coaching purposes. The organizer of the online session is usually one of multiple users participating in the online session, but may also be a different person who does not participate in the online session.

[0047] In this way, the host of an online session can grasp the status of participants who are not displayed on the screen in an environment where an online session is being held with multiple people.

[0048] <Functional configuration example 3> Fig. 6 is a block diagram showing an example of the configuration according to this embodiment. As shown in Fig. 6, in the video session evaluation system of this embodiment, functions similar to those in the first embodiment described above are given the same reference numerals and descriptions thereof may be omitted.

[0049] The system according to this embodiment includes a camera unit that captures video footage of the video session, a microphone unit that captures audio, an analysis unit that analyzes and evaluates the video, an object generation unit that generates a display object (described later) based on information obtained by evaluating the acquired video, and a display unit that displays both the video footage of the video session and the display object while the video session is being executed.

[0050] As explained above, the analysis unit includes a video image acquisition unit 11, a biological reaction analysis unit 12, a peculiar determination unit 13, a related event identification unit 14, a clustering unit 15, and an analysis result notification unit 16. The functions of each element are as described above.

[0051] 7, the object generation unit, based on the analysis result of the video acquired from the video session by the analysis unit, displays an object 50 indicating the recognized face portion and information 100 indicating the analyzed and evaluated content as necessary, superimposed on the video. When multiple faces appear in the video, the object 50 may identify and display the faces of all of the multiple people.

[0052] Furthermore, even if the camera function of the video session is disabled on the other party's device (i.e., the camera is disabled by software within the video session application, rather than by physically covering the camera, etc.), if the other party's face is recognized by the other party's camera, the object 50 or the object 100 may be displayed in the area where the other party's face is located. This allows both parties to confirm that the other party is in front of the device even if the camera function is turned off. In this case, for example, the video session application may hide information acquired from the camera, while displaying only the object 50 or the object 100 corresponding to the face recognized by the analysis unit. Furthermore, the video information acquired from the video session and the information recognized and obtained by the analysis unit may be separated into different display layers, and the layer related to the former information may be hidden.

[0053] When there is an area for displaying multiple moving images, the object 50 or the object 100 may be displayed in all areas or only in a part of the areas. For example, as shown in Fig. 8, the object 50 or the object 100 may be displayed only in the moving image on the guest side.

[0054] The embodiments of the invention described above in Basic Configuration Example 1 to Basic Configuration Example 3 may be realized as a single device, or may be realized by a plurality of devices (e.g., cloud servers) partially or entirely connected via a network. For example, the control unit 110 and storage 130 of each terminal 10 may be realized by different servers connected to each other via a network. That is, this system includes user terminals 10 and 20, a video session service terminal 30 that provides two-way video sessions to the user terminals 10 and 20, and an evaluation terminal 40 that evaluates the video sessions. The following variations and combinations of configurations are possible: (1) All processing is done on the user's device As shown in Figure 8, by performing processing by the analysis unit on the terminal where the video session is being held, the analysis and evaluation results can be obtained simultaneously (in real time) with the time the video session is being held (although a certain amount of processing power is required). (2) Processing on the user terminal and the evaluation terminal 9, an analysis unit may be provided in an evaluation terminal connected via a network, etc. In this case, the video captured by the user terminal is shared with the evaluation terminal simultaneously with or after the video session, and after being analyzed and evaluated by the analysis unit in the evaluation terminal, information on objects 50 and 100 is shared with the user terminal together with or separately from the video data (i.e., information including at least the analysis data) and displayed on the display unit.

[0055] The following system is realized using each of the configurations of the above-described functional configuration examples 1 to 3 or a combination thereof.

[0056] <Embodiment> A video analysis system (hereinafter simply referred to as the "system") according to an embodiment of the present invention analyzes the reactions of participants in an online session based on video images obtained by filming all or specific participants in the session. The analysis may be performed regardless of whether the participants are visible on the screen during the online session.

[0057] As shown in FIG. 10, the system according to this embodiment includes a storage unit that stores employee video data, a classification unit, a score calculation unit, and an output unit. A video data acquisition unit (not shown) acquires employee video data stored in the storage unit and inputs it to the classification unit. The classification unit classifies the online session based on the video data as either an internal conference or an external conference. The analysis unit analyzes changes in the biological responses of participants for each classified conference based on the video data acquired by the video data acquisition unit (see also FIGS. 3 to 5).

[0058] The score calculation unit calculates a predetermined score for each classification of the online session by referring to the analysis results by the analysis unit. The score may be calculated based on quantitative information about the meeting, such as the smile rate, number of comments, frequency of comments, duration of comments, number of unique speakers, etc.

[0059] The classification unit according to this embodiment refers to the facial images of employees who have been registered in advance, and at least classifies the session as either an internal online session that includes the employee or an external online session that does not include the employee. For example, if there are multiple participants, the classification can be made between cases where all participants are employees and cases where there are non-employees, thereby classifying the former as an internal conference and the latter as an external conference.

[0060] As shown in FIG. 11, the score calculation unit performs the above-described analysis of participants for each of the internal conference and the external conference, and calculates the scores.

[0061] For example, the score of an internal meeting (a score that indicates positive reactions, such as a smile score) can be an indicator of the openness of a company. Also, as shown in Figure 12, by comparing the score with other companies, you can objectively analyze the openness of your company.

[0062] Furthermore, as shown in FIG. 13, if there are external participants, it is possible to store video images of the external participants and calculate a score in the same way to evaluate customer satisfaction.

[0063] The processes described herein using flowchart diagrams do not necessarily have to be performed in the order shown, some process steps may be performed in parallel, additional process steps may be employed, and some process steps may be omitted.

[0064] The above-described embodiments may be combined as appropriate. Furthermore, the effects described in this specification are merely descriptive or exemplary and are not limiting. In other words, the technology according to the present disclosure may achieve other effects that are apparent to those skilled in the art from the description of this specification, in addition to or in place of the above-described effects. [Explanation of symbols]

[0065] 10, 20 user terminals 30 Video Session Service Terminals 40 Evaluation Devices

Claims

1. A video analysis system that analyzes the reactions of participants based on video images obtained by photographing the participants in an environment where an online session is held by multiple people, a video acquisition unit that acquires video images obtained by photographing the participants during the online session; an analysis unit that analyzes changes in biological reactions of the participant based on the moving images acquired by the moving image acquisition unit; a classification unit that classifies the type of the online session; a score calculation unit that calculates a predetermined score for each classification of the online session by referring to the analysis result by the analysis unit; Equipped with Video image analysis system.

2. The video analysis system according to claim 1, the classification unit refers to a face image of a pre-registered employee and at least classifies the session into an internal online session including the employee and an external online session not including the employee; the score calculation unit calculates the analysis as a statistical value for each of the plurality of internal online sessions and the plurality of external online sessions; Video image analysis system.

Images