Online examination invigilation system based on image recognition

Abstract

The invention relates to the technical field of image recognition, in particular to an online examination invigilation system based on image recognition, and the system comprises an image collection unit; the image analysis unit is used for determining the video state of the to-be-analyzed video according to the video duration reference value and the fuzzy reference value and determining an analysis mode according to the video state of the to-be-analyzed video; the association processing unit is used for determining the association state of the fuzzy frames according to the distance interval reference values between the fuzzy frames and determining a frame extraction point setting mode according to the association state of the fuzzy frames; the adjustment analysis unit is used for determining the category of the video frame according to the limb point change degree and the limb point change quantity reference value; and the adjustment processing unit is used for determining an adjustment mode according to the video frame aggregation area. According to the method, the selection positions of the video frames in the sports examination with complex actions can be optimized, so that the selection number of the video frames and the image quality effectively meet examination judgment requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of image recognition, and in particular to an online examination invigilation system based on image recognition. Background Art

[0002] With the rapid development of educational informatization, online examinations have become the new normal. For online sports examinations such as aerobics exams, in an online proctoring environment, due to the need to monitor the examination status of multiple candidates at the same time, system resources and the energy of the proctors are limited. Therefore, it is impossible to monitor every movement of each candidate in real time and in detail. In order to overcome the above limitations, the proctors will select representative video frames for evaluation, but due to the continuity and dynamics of aerobics movements, the selected video frames often have defects such as blurred frames and weak representativeness, which cannot fully reflect the test level of the candidates. Therefore, how to optimize the selection position of video frames in the aerobics exam and select video frames that can reflect the test level of candidates is a technical problem that needs to be solved urgently by technical personnel in this field.

[0003] Chinese patent publication number CN117975333A discloses a physical examination system for detecting students' academic level, including: video and image acquisition equipment to shoot video data of candidates during the physical examination, the shooting range of the video and image acquisition equipment covers the entire area of ​​the score detection mat; the score detection mat measures the test scores of candidates during the test, and different score lines are marked on the score detection mat; the edge computing device is used to receive the video data shot by the video and image acquisition equipment, and analyze the video data according to the detection algorithm to obtain the test results, the detection algorithm includes the line stepping detection algorithm, the landing detection algorithm, the human body posture detection algorithm and the score calculation, and the test results include fouls and normal scores; the result output device is used to output the test results. It can be seen that the above technical solution has the following problems: the video frame selection position in the physical examination with complex movements is not optimized, and the number of video frames selected and the image quality of the video frames cannot effectively meet the test evaluation requirements. Summary of the invention

[0004] To this end, the present invention provides an online examination invigilation system based on image recognition, so as to overcome the problems in the prior art that the video frame selection position in sports examinations with complex movements is not optimized, and the number of selected video frames and the image quality of the video frames cannot effectively meet the examination evaluation requirements.

[0005] To achieve the above object, the present invention provides an online examination invigilation system based on image recognition, comprising:

[0006] An image acquisition unit, used to acquire video frame information of the video to be analyzed;

[0007] An image analysis unit connected to the image acquisition unit, configured to determine a video state of the video to be analyzed according to a video duration reference value and a fuzzy reference value, and to determine an analysis method according to the video state of the video to be analyzed;

[0008] An association processing unit connected to the image acquisition unit and the image analysis unit, for determining an association state of blur frames according to a distance interval reference value between blur frames, and determining a frame extraction point setting method according to the association state of blur frames;

[0009] An adjustment and analysis unit connected to the image acquisition unit and the image analysis unit, for determining a video frame category according to a limb point change degree and a limb point change quantity reference value;

[0010] An adjustment processing unit is connected to the adjustment analysis unit and is used to determine an adjustment method according to the video frame aggregation area, such as adjusting the number of frame sampling points in a type of frame aggregation area according to the similarity of the two types of frame images closest to the two ends of the type of frame aggregation area, or adjusting the frame sampling point interval of the video to be analyzed according to the length similarity of the two types of frame aggregation areas.

[0011] Furthermore, the image analysis unit determines an analysis method according to a video state of the video to be analyzed, wherein, in a first video state, the analysis method is a first analysis method that determines a frame extraction point setting method according to a correlation state of blurred frames; in a second video state, the analysis method is a second analysis method that determines an adjustment method according to a first-class frame aggregation area and a second-class frame aggregation area.

[0012] Further, the image analysis unit determines the video state of the video to be analyzed according to the video duration reference value and the fuzzy reference value;

[0013] The video state of the video to be analyzed includes a first video state in which the video length reference value is greater than a preset video length reference value or the blur reference value is greater than a preset blur reference value, and a second video state in which the video length reference value is less than or equal to the preset video length reference value and the blur reference value is less than or equal to the preset blur reference value.

[0014] Further, the association processing unit responds to the first analysis mode and determines the frame extraction point setting mode according to the association state of the blurred frame;

[0015] If the association state is the first preset association state, the association analysis unit determines that the frame extraction point setting method is to perform segmentation processing on the video to be analyzed according to the fuzzy frame position to obtain a number of associated fuzzy frame segments, and perform frame extraction point compensation on each associated fuzzy frame segment;

[0016] If the association state is the second preset association state, the association analysis unit determines that the frame extraction point setting method is to perform frame extraction point compensation for the video segments between adjacent blurred frames.

[0017] Furthermore, the association processing unit performs segmentation processing on the video to be analyzed according to the fuzzy frame positions to obtain a plurality of associated fuzzy frame segments, and a time interval reference value between each fuzzy frame in any single associated fuzzy frame segment is less than a preset time interval reference value;

[0018] The segmentation processing includes: performing segmentation analysis on each blurred frame in turn according to the time sequence of the video to be analyzed; when performing segmentation analysis on a single blurred frame, recording the blurred frame as a target blurred frame; detecting a time interval reference value between each blurred frame after the time sequence of the target blurred frame; if the time interval reference value between the blurred frame after the time sequence of the target blurred frame and the blurred frame is less than a preset time interval reference value, recording it as an associated blurred frame of the target blurred frame; if the time interval reference value between the blurred frame after the time sequence of the target blurred frame and the blurred frame is greater than or equal to the preset time interval reference value, stopping the segmentation analysis of the target blurred frame, and uniformly recording the target blurred frame and its corresponding associated blurred frame as an associated blurred frame segment.

[0019] Further, the association processing unit determines the association state of the blurred frames according to the distance interval reference value between the blurred frames;

[0020] The first preset association state is that there are a number of adjacent blurred frames whose distance interval reference value is equal to the frame sampling point interval in the video to be analyzed;

[0021] The second preset association state is that the reference values ​​of the distance intervals of the blur frames in the video to be analyzed are all greater than the frame extraction point intervals;

[0022] The frame sampling point interval is the time length between two adjacent frame sampling points in the video to be analyzed.

[0023] Furthermore, the adjustment analysis unit determines the video frame category based on the limb point change degree and the limb point change quantity reference value, the video frame category including a type of frame in which the limb point change degree is less than or equal to a preset limb point change degree and the limb point change quantity reference value is less than or equal to a preset limb point change quantity reference value, and a type of frame in which the limb point change degree is greater than the preset limb point change degree or the limb point change quantity reference value is greater than the preset limb point change quantity reference value.

[0024] Further, the adjustment processing unit responds to the second analysis method and determines an adjustment method according to the video frame aggregation area, the adjustment method including a first adjustment method for adjusting the number of frame extraction points of the first frame aggregation area according to the similarity of the second frame images closest to the two ends of the first frame aggregation area, and a second adjustment method for adjusting the frame extraction point interval of the video to be analyzed according to the length similarity of the second frame aggregation area;

[0025] The video frame aggregation area includes a first-class frame aggregation area and a second-class frame aggregation area. The confirmation method of the first-class frame aggregation area and the second-class frame aggregation area is:

[0026] When multiple consecutive adjacent video frames are all class I frames, these video frames are recorded as target class I frames, and the class I frame aggregation area is the video area between the start target class I frame and the end target class I frame in the time sequence;

[0027] When multiple consecutive adjacent video frames are all Class II frames, these video frames are recorded as target Class II frames, and the Class II frame aggregation area is the video area between the starting target Class II frame and the ending target Class II frame in time sequence.

[0028] Furthermore, the adjustment processing unit responds to the first adjustment mode and increases or decreases the number of frame extraction points in the first frame aggregation area according to the similarity of the second frame images closest to the two ends of the first frame aggregation area;

[0029] There is a negative correlation between the number of frame extraction points in the first-class frame aggregation area and the similarity of the second-class frame images at both ends of the first-class frame aggregation area.

[0030] Furthermore, the adjustment processing unit responds to the second adjustment mode, and if there are multiple second-type frame clustering regions whose length similarity is greater than a preset length similarity in the video to be analyzed, the frame sampling point interval of the video to be analyzed is reduced and adjusted;

[0031] The relationship between the reduction value of the frame extraction point interval and the representativeness of the second type of frame image is positively correlated.

[0032] Compared with the prior art, the beneficial effect of the present invention lies in that, in the technical scheme of the present invention, the image analysis unit effectively reflects the video state of the video to be analyzed according to the video length reference value and the blur reference value, and effectively reflects the complexity of the physical examination movements in the video to be analyzed through the video state of the video to be analyzed, and adaptively selects different analysis methods, so that the selection of analysis method is more in line with the actual work scenario, avoiding the problem that the number of selected video frames and the image quality cannot effectively meet the test evaluation requirements due to the video to be analyzed being too long and having too many blurry frames, thereby improving the accuracy and representativeness of the test scoring.

[0033] Furthermore, the association processing unit in the present invention determines the association status of the blurred frames according to the reference value of the distance interval between each blurred frame, effectively reflects the distribution of blurred frames in the video to be analyzed according to the association status of the blurred frames, and selects different frame extraction point setting methods according to actual conditions, thereby avoiding the problem that the number of video frames cannot effectively meet the actual examination requirements, thereby improving the accuracy of physical examination scoring.

[0034] Furthermore, the associated processing unit in the present invention performs segmentation processing on the video to be analyzed according to the blurred frame position, ensuring that when frame extraction point compensation is performed on the associated blurred frame segment, different numbers of frame extraction points can be adaptively set according to the length of the associated blurred frame segment, thereby making the number of selected video frames effectively meet the test evaluation requirements.

[0035] Furthermore, the adjustment analysis unit in the present invention determines the video frame category according to the limb point change degree and the limb point change quantity reference value, determines the first type of frame aggregation area and the second type of frame aggregation area through the video frame category, and determines different adjustment methods according to the first type of frame aggregation area and the second type of frame aggregation area, thereby avoiding the problem that the selected position of the video frame cannot effectively meet the test evaluation requirements, thereby improving the representativeness of the video frame.

[0036] Furthermore, the adjustment processing unit in the present invention increases or decreases the number of frame extraction points in the first frame aggregation area according to the similarity of the second frame images closest to both ends of the first frame aggregation area, so that the system can flexibly adjust the number of frame extraction points according to the similarity of the second frame images closest to both ends of the first frame aggregation area, thereby ensuring that the repeated extraction of redundant information can be avoided when the similarity of the two frame images is high, and also ensuring that the representative information of the examinee's gymnastics exercise is not omitted when the similarity of the two frame images is low, thereby improving the representativeness of the video frame selection.

[0037] Furthermore, the adjustment processing unit in the present invention reduces the frame sampling point interval of the video to be analyzed, increases the number of selected video frames, avoids the problem of insufficient representativeness of the video frames due to the high similarity of the two types of frame clustering areas in the video to be analyzed, and thus effectively meets the test evaluation requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a unit connection diagram of the online examination invigilation system based on image recognition of the present invention;

[0039] Figure 2 This is a flow chart of the present invention for determining an analysis method according to a video state of a video to be analyzed;

[0040] Figure 3 A flow chart of the present invention for determining the video state of a video to be analyzed according to a video duration reference value and a fuzzy reference value;

[0041] Figure 4 The present invention is a flow chart of determining an adjustment method according to a video frame aggregation area. DETAILED DESCRIPTION

[0042] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0043] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0044] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0045] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0046] See also Figures 1 to 4 As shown, the present invention provides an online examination invigilation system based on image recognition, comprising:

[0047] An image acquisition unit, used to acquire video frame information of the video to be analyzed;

[0048] An image analysis unit connected to the image acquisition unit, configured to determine a video state of the video to be analyzed according to a video duration reference value and a fuzzy reference value, and to determine an analysis method according to the video state of the video to be analyzed;

[0049] An association processing unit connected to the image acquisition unit and the image analysis unit, for determining an association state of blur frames according to a distance interval reference value between blur frames, and determining a frame extraction point setting method according to the association state of blur frames;

[0050] An adjustment and analysis unit connected to the image acquisition unit and the image analysis unit, for determining a video frame category according to a limb point change degree and a limb point change quantity reference value;

[0051] An adjustment processing unit is connected to the adjustment analysis unit and is used to determine an adjustment method according to the video frame aggregation area, such as adjusting the number of frame sampling points in a type of frame aggregation area according to the similarity of the two types of frame images closest to the two ends of the type of frame aggregation area, or adjusting the frame sampling point interval of the video to be analyzed according to the length similarity of the two types of frame aggregation areas.

[0052] The application scenario of the present invention is an online test for the prescribed actions in the physical examination. The frame extraction rule initially set by the present invention is: a frame extraction point is set every t seconds in the time sequence of the video to be analyzed, wherein the value of t can be set by the user according to actual needs. The greater the user's demand for the accuracy of the video score to be analyzed, the smaller the value of t is. A value of t is provided, t=10. For a single frame extraction point, the video image extracted at the frame extraction point is the video frame corresponding to the frame extraction point, and the blurred frame is a video frame with a blur greater than a preset blur; each video frame image is converted into a grayscale image, and then a Laplace operator template is used to perform a convolution operation on the grayscale image to obtain a new image, and the sum of the absolute values ​​of the pixel values ​​of each pixel in the new image is used as the blur. The value of the preset blur can be determined by the user according to the historical use record. The greater the user's demand for the accuracy of the video score to be analyzed, the greater the value of the preset blur. A value of the preset blur is provided, and the preset blur is the maximum value of the blur of the blurred frame in the video whose scoring accuracy meets the user's needs during the historical use process.

[0053] Specifically, the image analysis unit determines the analysis method according to the video state of the video to be analyzed, wherein, in the first video state, the analysis method is a first analysis method that determines the frame extraction point setting method according to the association state of the blurred frame; in the second video state, the analysis method is a second analysis method that determines the adjustment method according to the first type of frame aggregation area and the second type of frame aggregation area.

[0054] Specifically, the image analysis unit determines the video state of the video to be analyzed according to the video duration reference value and the fuzziness reference value;

[0055] The video state of the video to be analyzed includes a first video state in which the video length reference value is greater than a preset video length reference value or the blur reference value is greater than a preset blur reference value, and a second video state in which the video length reference value is less than or equal to the preset video length reference value and the blur reference value is less than or equal to the preset blur reference value.

[0056] The video duration reference value is the time length of the video to be analyzed, and the blur reference value = the number of blur frames of the video to be analyzed / the total number of video frames of the video to be analyzed;

[0057] The values ​​of the preset video length reference value and the preset fuzzy reference value can be determined by the user based on the historical usage records of the system. The greater the user's demand for the accuracy of the score of the video to be analyzed, the smaller the values ​​of the preset video length reference value and the preset fuzzy reference value. A value of a preset video length reference value is provided, and the preset video length reference value is 15 minutes. A value of a preset fuzzy reference value is provided. The fuzzy reference values ​​corresponding to videos with the same video length as the video to be analyzed during historical usage are detected, and the average value of the fuzzy reference values ​​whose scoring accuracy meets the user's needs is recorded as the preset fuzzy reference value.

[0058] Specifically, the association processing unit responds to the first analysis mode and determines the frame extraction point setting mode according to the association state of the blurred frame;

[0059] If the association state is the first preset association state, the association analysis unit determines that the frame extraction point setting method is to perform segmentation processing on the video to be analyzed according to the fuzzy frame position to obtain a number of associated fuzzy frame segments, and perform frame extraction point compensation on each associated fuzzy frame segment;

[0060] If the association state is the second preset association state, the association analysis unit determines that the frame extraction point setting method is to perform frame extraction point compensation for the video segments between adjacent blurred frames.

[0061] The frame point compensation is to increase the number of frame points and reduce the frame point interval by increasing the number of frame points, so that the number of video frames can meet the actual test needs. The higher the user's demand for the scoring accuracy of video actions in the test, the smaller the value of the frame point interval set during frame point compensation. The reduction value of the frame point interval is positively correlated with the scoring accuracy of video actions in the test.

[0062] Specifically, the association processing unit performs segmentation processing on the video to be analyzed according to the fuzzy frame position to obtain a number of associated fuzzy frame segments, and the time interval reference value between each fuzzy frame in any single associated fuzzy frame segment is less than a preset time interval reference value;

[0063] The segmentation processing includes: performing segmentation analysis on each blurred frame in turn according to the time sequence of the video to be analyzed; when performing segmentation analysis on a single blurred frame, recording the blurred frame as a target blurred frame; detecting a time interval reference value between each blurred frame after the time sequence of the target blurred frame; if the time interval reference value between the blurred frame after the time sequence of the target blurred frame and the blurred frame is less than a preset time interval reference value, recording it as an associated blurred frame of the target blurred frame; if the time interval reference value between the blurred frame after the time sequence of the target blurred frame and the blurred frame is greater than or equal to the preset time interval reference value, stopping the segmentation analysis of the target blurred frame, and uniformly recording the target blurred frame and its corresponding associated blurred frame as an associated blurred frame segment.

[0064] When a related fuzzy frame segment is confirmed to be complete, the segmentation analysis is continued for a fuzzy frame that is closest to the related fuzzy frame segment in time sequence.

[0065] The time interval reference value is the time length between the frame extraction point where the blurred frame is located and the frame extraction point where the adjacent blurred frame in the time sequence is located;

[0066] The user can determine the value of the preset time interval reference value based on the historical usage records of the system. The greater the user's demand for the accuracy of the score of the video to be analyzed, the smaller the value of the preset time interval reference value. A value of the preset time interval reference value is provided. The preset time interval reference value is the maximum value of the time interval reference value in the associated blurred frame segments of each video whose scoring accuracy meets the user's demand in the historical usage records.

[0067] Specifically, the association processing unit determines the association state of the blurred frames according to the distance interval reference value between the blurred frames;

[0068] The first preset association state is that there are a number of adjacent blurred frames whose distance interval reference value is equal to the frame sampling point interval in the video to be analyzed;

[0069] The second preset association state is that the reference values ​​of the distance intervals of the blur frames in the video to be analyzed are all greater than the frame extraction point intervals;

[0070] The frame sampling point interval is the time length between two adjacent frame sampling points in the video to be analyzed.

[0071] The reference value of the distance interval between blur frames is the interval time between the frame extraction points corresponding to two adjacent blur frames.

[0072] Specifically, the adjustment analysis unit determines the video frame category based on the limb point change degree and the limb point change quantity reference value. The video frame category includes a type of frame in which the limb point change degree is less than or equal to a preset limb point change degree and the limb point change quantity reference value is less than or equal to a preset limb point change quantity reference value, and a type of frame in which the limb point change degree is greater than a preset limb point change degree or the limb point change quantity reference value is greater than a preset limb point change quantity reference value.

[0073] It can be understood that the video frame is an image with a rectangular shape, and a rectangular coordinate system is established with the lower left corner of the rectangle as the coordinate origin, the straight line extending to the right from the coordinate origin along the bottom edge of the rectangle as the x-axis, and the straight line extending upward from the coordinate origin along the left side of the rectangle as the y-axis. The limb point position is the coordinate of the limb point in the rectangular coordinate system. For a single limb point, the shortest distance between the limb point position and the corresponding limb point position in the standard state is recorded as the change distance. The standard state is the state in which the examinee in the video to be identified maintains a military posture and stands. The limb point change degree is the sum of the change distances of each limb point, and the reference value of the limb point change number is the number of limb points whose limb point positions have changed compared with the limb point positions in the standard state; randomly select a point on each joint of the human body, and these points are limb points, including head, shoulders, elbows, wrists, hips, knees and ankles; the identification of limb points can be determined by machine vision and deep learning networks, which is easy for technicians in this field to understand and will not be elaborated here.

[0074] The values ​​of the preset limb point change degree and the preset limb point change number reference value can be determined by the user based on the historical usage records of the system. The greater the user's demand for the accuracy of the video score to be analyzed, the smaller the values ​​of the preset limb point change degree and the preset limb point change number. A value of the preset limb point change degree is provided, and the preset limb point change degree is 0.5m. A value of the preset limb point change number reference value is provided, and the preset limb point change number reference value is 2.

[0075] Specifically, the adjustment processing unit responds to the second analysis method and determines the adjustment method according to the video frame aggregation area, the adjustment method including a first adjustment method for adjusting the number of frame extraction points of the first frame aggregation area according to the similarity of the second frame images closest to the two ends of the first frame aggregation area, and a second adjustment method for adjusting the frame extraction point interval of the video to be analyzed according to the length similarity of the second frame aggregation area.

[0076] The video frame aggregation area includes a first-class frame aggregation area and a second-class frame aggregation area. The confirmation method of the first-class frame aggregation area and the second-class frame aggregation area is:

[0077] When multiple consecutive adjacent video frames are all class I frames, these video frames are recorded as target class I frames, and the class I frame aggregation area is the video area between the start target class I frame and the end target class I frame in the time sequence;

[0078] When multiple consecutive adjacent video frames are all Class II frames, these video frames are recorded as target Class II frames, and the Class II frame aggregation area is the video area between the starting target Class II frame and the ending target Class II frame in time sequence.

[0079] When the video frame aggregation area is a first type of frame aggregation area, the adjustment method is the first adjustment method; when the video frame aggregation area is a second type of frame aggregation area, the adjustment method is the second adjustment method.

[0080] The calculation formula of the similarity φ of the two types of frame images closest to the two ends of the one type of frame clustering area is: φ = α × ω1 + β × ω2, where α is the proportion of the same changed limb points, ω1 is the weight coefficient corresponding to the proportion of the same changed limb points, β is the difference in the degree of change of the limb points, and ω2 is the weight coefficient corresponding to the difference in the degree of change of the limb points;

[0081] The proportion of the same changed limb points α = the reference value of the number of the same changed limb points / the maximum value of the reference value of the number of changed limb points in the two types of frame images closest to the two ends of the aggregation area of ​​the first type of frame;

[0082] The reference value of the number of identical changed limb points is the number of changed limb points with the same coordinates in the standard state in the two types of frame images closest to the two ends of the one type of frame aggregation area; the reference value of the number of changed limb points is the number of limb points whose positions in the video frame are changed from the corresponding limb point positions in the standard state;

[0083] The limb point change difference β is the absolute value of the difference between the limb point change of the second-class frame image closest to the left end of the first-class frame aggregation area and the limb point change of the second-class frame image closest to the right end of the first-class frame aggregation area;

[0084] It can be understood that the weight coefficients corresponding to the proportion of limb points with the same change and the weight coefficients corresponding to the difference in the degree of change of limb points are set in advance by the user, and one value of ω1 and ω2 is provided, ω1=0.5, ω2=-0.5.

[0085] The length similarity of the second-class frame aggregation area = the maximum value of the length of each second-class frame aggregation area - the minimum value of the length of each second-class frame aggregation area. The length of the second-class frame aggregation area is the time length of the video area between the starting target second-class frame and the ending target second-class frame in time sequence.

[0086] Specifically, the adjustment processing unit responds to the first adjustment mode and increases or decreases the number of frame extraction points in the first frame aggregation area according to the similarity of the second frame images closest to the two ends of the first frame aggregation area;

[0087] There is a negative correlation between the number of frame extraction points in the first-class frame aggregation area and the similarity of the second-class frame images at both ends of the first-class frame aggregation area.

[0088] When the similarity of the two types of frame images closest to the two ends of the one type of frame aggregation area is greater than the preset image similarity, the number of frame extraction points in the one type of frame aggregation area is reduced;

[0089] When the similarity of the two types of frame images closest to the two ends of the one type of frame aggregation area is less than the preset image similarity, the number of frame extraction points in the one type of frame aggregation area is increased and adjusted;

[0090] The value of the preset image similarity can be determined by the user based on the historical usage records of the system. The greater the user's demand for the accuracy of the score of the video to be analyzed, the greater the value of the preset image similarity. A value of the preset image similarity is provided, and the preset image similarity is the average value of the similarity of the two types of frame images closest to the two ends of the first type of frame aggregation area of ​​each video whose scoring accuracy meets the user's demand in the historical usage records.

[0091] Specifically, the adjustment processing unit responds to the second adjustment mode, and if there are multiple second-type frame clustering regions whose length similarity is greater than the preset length similarity in the video to be analyzed, the frame extraction point interval of the video to be analyzed is reduced;

[0092] The relationship between the reduction value of the frame extraction point interval and the representativeness of the second type of frame image is positively correlated.

[0093] The value of the preset length similarity can be determined by the user based on the historical usage records of the system. The greater the user's demand for the accuracy of the score of the video to be analyzed, the larger the value of the preset length similarity is. A value of the preset length similarity is provided, which is the average length similarity of the second type of frame clustering areas of each video whose score accuracy meets the user's demand in the historical usage records.

[0094] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0095] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An online examination invigilation system based on image recognition, characterized in that: include: An image acquisition unit, used to acquire video frame information of the video to be analyzed; An image analysis unit connected to the image acquisition unit, configured to determine a video state of the video to be analyzed according to a video duration reference value and a fuzzy reference value, and to determine an analysis method according to the video state of the video to be analyzed; An association processing unit connected to the image acquisition unit and the image analysis unit, for determining an association state of blur frames according to a distance interval reference value between blur frames, and determining a frame extraction point setting method according to the association state of blur frames; An adjustment and analysis unit connected to the image acquisition unit and the image analysis unit, for determining a video frame category according to a limb point change degree and a limb point change quantity reference value; An adjustment processing unit is connected to the adjustment analysis unit and is used to determine an adjustment method according to the video frame aggregation area, such as adjusting the number of frame sampling points in a type of frame aggregation area according to the similarity of the two types of frame images closest to the two ends of the type of frame aggregation area, or adjusting the frame sampling point interval of the video to be analyzed according to the length similarity of the two types of frame aggregation areas.

2. The online examination invigilation system based on image recognition according to claim 1, characterized in that: The image analysis unit determines an analysis method according to a video state of the video to be analyzed, wherein, in a first video state, the analysis method is a first analysis method for determining a frame extraction point setting method according to an associated state of blurred frames; in a second video state, the analysis method is a second analysis method for determining an adjustment method according to a first-class frame aggregation area and a second-class frame aggregation area.

3. The online examination invigilation system based on image recognition according to claim 2 is characterized in that: The image analysis unit determines the video state of the video to be analyzed according to the video duration reference value and the fuzzy reference value; The video state of the video to be analyzed includes a first video state in which the video length reference value is greater than a preset video length reference value or the blur reference value is greater than a preset blur reference value, and a second video state in which the video length reference value is less than or equal to the preset video length reference value and the blur reference value is less than or equal to the preset blur reference value.

4. The online examination invigilation system based on image recognition according to claim 3 is characterized in that: The association processing unit responds to the first analysis mode and determines the frame extraction point setting mode according to the association state of the blurred frame; If the association state is the first preset association state, the association analysis unit determines that the frame extraction point setting method is to perform segmentation processing on the video to be analyzed according to the fuzzy frame position to obtain a number of associated fuzzy frame segments, and perform frame extraction point compensation on each associated fuzzy frame segment; If the association state is the second preset association state, the association analysis unit determines that the frame extraction point setting method is to perform frame extraction point compensation for the video segments between adjacent blurred frames.

5. The online examination invigilation system based on image recognition according to claim 4 is characterized in that: The association processing unit performs segmentation processing on the video to be analyzed according to the fuzzy frame positions to obtain a number of associated fuzzy frame segments, and the time interval reference value between each fuzzy frame in any single associated fuzzy frame segment is less than a preset time interval reference value; The segmentation processing includes: performing segmentation analysis on each blurred frame in turn according to the time sequence of the video to be analyzed; when performing segmentation analysis on a single blurred frame, recording the blurred frame as a target blurred frame; detecting a time interval reference value between each blurred frame after the time sequence of the target blurred frame; if the time interval reference value between the blurred frame after the time sequence of the target blurred frame and the blurred frame is less than a preset time interval reference value, recording it as an associated blurred frame of the target blurred frame; if the time interval reference value between the blurred frame after the time sequence of the target blurred frame and the blurred frame is greater than or equal to the preset time interval reference value, stopping the segmentation analysis of the target blurred frame, and uniformly recording the target blurred frame and its corresponding associated blurred frame as an associated blurred frame segment.

6. The online examination invigilation system based on image recognition according to claim 4, characterized in that: The association processing unit determines the association state of the blurred frames according to the distance interval reference value between the blurred frames; The first preset association state is that there are a number of adjacent blurred frames whose distance interval reference value is equal to the frame sampling point interval in the video to be analyzed; The second preset association state is that the reference values ​​of the distance intervals of the blur frames in the video to be analyzed are all greater than the frame extraction point intervals; The frame sampling point interval is the time length between two adjacent frame sampling points in the video to be analyzed.

7. The online examination invigilation system based on image recognition according to claim 3 is characterized in that: The adjustment analysis unit determines the video frame category based on the limb point change degree and the limb point change quantity reference value. The video frame category includes a type of frame in which the limb point change degree is less than or equal to a preset limb point change degree and the limb point change quantity reference value is less than or equal to a preset limb point change quantity reference value, and a type of frame in which the limb point change degree is greater than a preset limb point change degree or the limb point change quantity reference value is greater than a preset limb point change quantity reference value.

8. The online examination invigilation system based on image recognition according to claim 7, characterized in that: The adjustment processing unit responds to the second analysis method and determines an adjustment method according to the video frame aggregation area, the adjustment method including a first adjustment method for adjusting the number of frame extraction points of the first frame aggregation area according to the similarity of the second frame images closest to the two ends of the first frame aggregation area, and a second adjustment method for adjusting the frame extraction point interval of the video to be analyzed according to the length similarity of the second frame aggregation area; The video frame aggregation area includes a first-class frame aggregation area and a second-class frame aggregation area. The confirmation method of the first-class frame aggregation area and the second-class frame aggregation area is: When multiple consecutive adjacent video frames are all class I frames, these video frames are recorded as target class I frames, and the class I frame aggregation area is the video area between the start target class I frame and the end target class I frame in the time sequence; When multiple consecutive adjacent video frames are all Class II frames, these video frames are recorded as target Class II frames, and the Class II frame aggregation area is the video area between the starting target Class II frame and the ending target Class II frame in time sequence.

9. The online examination invigilation system based on image recognition according to claim 7, characterized in that: The adjustment processing unit responds to the first adjustment mode and increases or decreases the number of frame extraction points in the first frame aggregation area according to the similarity of the two types of frame images closest to the two ends of the first frame aggregation area; There is a negative correlation between the number of frame extraction points in the first-class frame aggregation area and the similarity of the second-class frame images at both ends of the first-class frame aggregation area.

10. The online examination invigilation system based on image recognition according to claim 7, characterized in that: The adjustment processing unit responds to the second adjustment mode, and if there are multiple second-type frame clustering regions whose length similarity is greater than the preset length similarity in the video to be analyzed, the frame extraction point interval of the video to be analyzed is reduced; The relationship between the reduction value of the frame extraction point interval and the representativeness of the second type of frame image is positively correlated.

Images