Methods for classifying media content

Abstract

The invention relates to a method for classifying media content to be displayed to a person in a moving vehicle, in which the media content is analyzed with regard to its dynamics, wherein the media content is classified via a classification system that includes a relationship between a dynamics of media content and the development of motion sickness in persons, and a value for a motion sickness probability when viewing this media content is displayed to the person.

Description

The invention relates to a method for classifying media content to be displayed to a person in a moving vehicle. Document DE 10 2017 206 740 A1 describes a method and a system for setting or adjusting a motion sickness profile of a vehicle occupant. A method for analyzing VR content with regard to its motion sickness effect is known from publication US 2020 / 183495 A1. Against this background, one task was to alert a person in a vehicle to the possibility of motion sickness. This problem is solved by a method with the features of the independent patent claim. Embodiments of the method are described in the dependent patent claims and the description. The method according to the invention is designed for classifying media content, such as a film or video, to be displayed to a person or occupant in a moving vehicle and / or means of transport. The media content is analyzed with regard to its dynamics, and classified using a classification system that includes and / or describes a relationship between the dynamics of media content and the development of motion sickness in people. A value indicating the probability of motion sickness when viewing this media content is then displayed to the person, for example, on a display panel in the vehicle. Media content is analyzed and classified to provide the classification system with regard to its contribution to the development of motion sickness. By visualizing the assignment of each piece of media content to a specific class with respect to the development of motion sickness, whereby the probability of motion sickness is assigned to each piece of media content and visually represented, a recommendation regarding the possible development of motion sickness can be presented to the individual. In this design, the person in the vehicle will be shown previously unknown media content, such as a video. This content can be pre-classified using a classification system that assigns a category to each piece of media content with regard to its potential to cause motion sickness. The resulting classification is then displayed to the person. Based on this, the person can decide whether to view the media content, for example, if the probability of motion sickness determined by the classification system is low, or to refrain from viewing it if the probability is too high. The procedure is performed on visual and / or optical media content that contains several temporally successive images, wherein the dynamics of the media content include a switching rate between images and / or a movement represented in, with or by several images. Additionally, an artificial intelligence-based algorithm can be developed as part of the classification system. This algorithm analyzes and evaluates media content based on its motion intensity and / or frame rate as potential parameters for motion sickness. The underlying assumption is that media content with higher motion intensity or increased frame rates leads to more motion sickness. This algorithm can be trained and / or learned to recognize the relationship between motion sickness and the probability of motion sickness. To provide, train, and / or teach the artificial intelligence-based classification system, several different people in vehicles, usually moving, are first shown a variety of familiar media content, each piece of media content exhibiting dynamics, and this dynamics is analyzed by artificial intelligence, with at least one piece of media content being classified by at least one person with regard to the motion sickness that arises and / or has arisen in them, with the classification system being supplemented, trained, and / or taught with regard to a correlation between the dynamics determined by analysis and the occurrence of motion sickness for the respective media content in individuals. In its implementation, the classification system provides a scale that indicates and / or displays the motion sickness probability of an analyzed media content between a minimum and a maximum value. This value is displayed to the person in the moving vehicle, for example, on a display panel or screen. If the person wishes to view the media content based on the motion sickness probability value, it can also be shown and / or presented to them via the display panel, which is typically located in the vehicle. As part of the process, the classification system is provided through training and continuously updated. This allows for the ongoing collection of new information about the relationships between media content and the development of motion sickness in individuals, which is then incorporated into the training of the classification system. The classification system is built during the training process. To provide information for training, each person can indicate, after finishing a film or video game, whether and / or to what extent they experienced motion sickness due to the dynamic media content presented, or how severely they suffer from motion sickness caused by the respective media content. The classification system for media content regarding its likelihood of triggering motion sickness, developed and / or used in this process, is not person-specific but media-specific. The media content of films is classified according to its probability of inducing motion sickness. This can be done in an iterative process in which individuals evaluate the media content of films with regard to its contribution to triggering motion sickness. The classification system, which assigns a value for motion sickness probability to each piece of media, can, for example, comprise a maximum of five stars. Media assigned only one star for a minimum value has a low probability of causing motion sickness or contributes little to its inducement. Conversely, media assigned five stars for a maximum value has a very high probability of causing motion sickness or contributes significantly to its inducement. Motion sickness probability values ​​according to the classification system can also be represented by traffic light colors or a simple scale ranging from, for example, 0 as the minimum value to, for example, 10 as the maximum value. Simultaneously, the classification system's algorithm is developed during the iterative process, analyzing media content based on technical and / or visually identifiable parameters, such as the number of cuts in a film or video, the frame rate, motion in multiple consecutive frames, and / or other general data, such as the film's or video's genre. Using this algorithm as part of the classification system, the artificial intelligence can automatically perform a preliminary assessment and / or classification of media content without requiring input from real people. If motion sickness is known or detected, recommendations or warnings regarding the potential impact of certain films on the development of motion sickness can be displayed on the vehicle's screen.specified, and / or output acoustically via a loudspeaker in the vehicle. The procedure can be performed for a passenger in a passenger car, either sitting in the front passenger seat and using a passenger-side display or sitting in the rear seat and using rear-seat entertainment. However, the procedure can also be performed for a driver, sitting in the driver's seat, for example, in an automated driving situation. The procedure can be performed for various vehicles and / or means of transport for people, such as a passenger car, a truck, or a train, where motion sickness could occur. It is understood that the features mentioned above and those to be explained below can be used not only in the combinations specified, but also in other combinations or on their own, without leaving the scope of the present invention. The invention is schematically illustrated in the drawing with reference to one embodiment and is described schematically and in detail with reference to the drawing. Fig. 1 shows a schematic flowchart of one embodiment of the method according to the invention. The flowchart shown in Fig. 1 is provided for a schematic representation of the embodiment of the method according to the invention, wherein the method here comprises a first step, which includes three sub-steps 1a, 1b, 1c, and a second step, which also includes three sub-steps 2a, 2b, 2c. In this embodiment of the method, media content, e.g., a film or video, intended to be displayed to a person on a screen in the interior of a vehicle, is classified with regard to the likelihood of motion sickness or kinetosis occurring when the person sitting inside views the media content. In the presented embodiment of the method, a classification system is provided in the three sub-steps 1a, 1b, and 1c to describe the relationship between motion sickness or the probability of motion sickness and media content, and / or the dependence of the probability of motion sickness on media content. In the first step (1a), a large number of dynamic media contents, such as videos or films, are analyzed with regard to technical parameters of the images within the media content, such as the refresh rate of the images and / or movements within several temporally successive images. In the second step (1b), a person's subjective well-being while viewing each analyzed piece of media content during a vehicle journey is recorded. In the subsequent third step (1c), the probability of motion sickness for the media content is calculated and classified, taking into account the correlation between the dynamics determined by the analysis in the first step (1a) and the well-being recorded in the second step (1b). A scale representing the probability of motion sickness for each analyzed piece of media content is then provided.This is carried out for a wide variety of media content, whereby the classification system is built and / or provided. An algorithm, as part of the classification system, is trained and / or educated regarding the relationship between the dynamics and the development of motion sickness for a large number of media contents. A subsequent second step is also divided into three sub-steps: 2a, 2b, and 2c. The classification system provided in the first step is used in this step. In a fourth sub-step, 2a, based on the first step, general objective criteria are determined for new media content intended to be displayed to a person in a moving vehicle on a screen. These criteria predict the probability of motion sickness when viewing this media content. Using the trained and / or learned algorithm of the classification system, a fifth sub-step, 2b, determines the probability of motion sickness when viewing the media content while traveling in the vehicle, without obtaining any feedback from the person.Furthermore, in a sixth sub-step 2c, viewing the media content is recommended if the probability of motion sickness is low, or discouraged if the probability of motion sickness is high. The individual's susceptibility to motion sickness is also taken into account. Furthermore, the vehicle's route segment, whose characteristics influence the susceptibility to motion sickness or the probability of motion sickness, is taken into account. The probability of motion sickness is lower the straighter the route segment and higher the more winding the segment. The occurrence of motion sickness in the individual can also be detected by a camera mounted in the vehicle. Optionally, the individual can also enter information regarding their probability of motion sickness while viewing media content into an input device. This information is used for on-the-job training and / or learning, and thus for updating the classification system and its algorithm. REFERENCE MARK: 1a, 1b, 1c sub-step 2a, 2b, 2c sub-step QUOTES INCLUDED IN THE DESCRIPTION This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature DE 10 2017 206 740 A1

[0002] US 2020 / 183495 A1

[0003]

Claims

Method for classifying media content to be displayed to a person in a moving vehicle, in which the media content is analyzed with regard to its dynamics, wherein the media content is classified via a classification system that includes a relationship between a dynamic of media content and the development of motion sickness in persons, and a value for a motion sickness probability when viewing this media content is displayed to the person. Method according to claim 1, for a media content comprising several temporally successive images, wherein the dynamics of the media content comprise a switching rate between images and / or a movement represented in several images. A method according to claim 1 or 2, wherein a plurality of known media content is initially presented to persons in vehicles, wherein each media content exhibits dynamics, wherein at least one media content is classified by at least one person with regard to the motion sickness experienced by them, wherein the classification system is trained with regard to a relationship between the dynamics and the occurrence of motion sickness for the respective media content. A method according to one of the preceding claims, wherein the classification system provides a scale that indicates the value for the motion sickness probability of a given media content between a minimum value and a maximum value. A method according to any of the preceding claims, wherein the classification system is provided.

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