Intelligent running evaluation method and system based on face recognition

Abstract

The invention discloses an intelligent running evaluation method and system based on face recognition. According to the method, firstly, intelligent acquisition equipment installed in a school playground running area carries out real-time face snapshot and preprocessing on students through a high-resolution camera and an edge calculation module, then face recognition is carried out by using a deep learning model, and data such as running mileage, duration and speed setting of the students are calculated; then, through real-time data processing and statistical analysis, the system generates an intelligent evaluation result, and finally, information of current running students and daily and weekly lists of the whole school are displayed through a user-friendly interface. The exercise enthusiasm of students is improved, and a convenient management tool is provided for teachers and managers.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a running intelligent evaluation method and system based on face recognition. Background Art

[0002] In traditional campus physical exercise management, student running records usually rely on manual timing or simple clock-in devices, which have many shortcomings. For example, manual timing is inefficient and prone to errors, and clock-in devices may not accurately record running data if students forget to clock in or maliciously damage them.

[0003] In addition, these traditional methods are unable to provide real-time statistics on detailed data such as running mileage, duration, and pace, nor are they able to conduct a comprehensive analysis and ranking of students’ running performance. Summary of the invention

[0004] Based on this, the embodiment of the present application provides an intelligent running evaluation method and system based on face recognition. This method uses intelligent collection equipment and face recognition technology to achieve accurate collection, real-time recording and efficient management of students' running data, thereby improving the scientific nature and management efficiency of campus physical exercise.

[0005] In a first aspect, a running intelligent evaluation method based on face recognition is provided, the method comprising:

[0006] Install intelligent acquisition equipment at each monitoring point in the running area, and capture facial images through the intelligent acquisition equipment; wherein the intelligent acquisition equipment at least includes a high-resolution camera and an edge computing device, the high-resolution camera is used to capture the face, and the edge computing device is used to perform preliminary face detection and preprocessing;

[0007] Each captured face image is recognized using a deep learning model, and is compared one by one with a pre-stored student face database to determine whether the student corresponding to each face image is the target to be tested. If the comparison is successful, the identity information of the successfully matched student is associated with the running record;

[0008] When the target is running, the face of the student passing through each monitoring point is captured to confirm the target, and the running data of the target is collected; the running data includes running mileage, duration and pace data;

[0009] The collected running data is processed and statistically analyzed in real time to generate intelligent evaluation results for the target to be tested.

[0010] Optionally, intelligent data collection equipment is installed at each monitoring point in the running area, and also includes:

[0011] Set the effective capture time. When the smart acquisition device is running, it will obtain the current time in real time and compare it with the set effective capture time. The smart acquisition device will start the capture process only when you enter the running area within the effective capture time.

[0012] Set a minimum number of captured photos. During the capture process, the intelligent collection device will count the photos captured by each student. When processing the captured data, it will check whether the number of captured photos of each student reaches the set minimum threshold. Only when the number of captured photos reaches or exceeds the set minimum number, the student's running data will be counted and recorded in the database.

[0013] Optionally, the edge computing device performs preliminary face detection and preprocessing, including:

[0014] After capturing an image through a high-resolution camera, the NPU algorithm is used to identify the face area in the image and determine its position and size;

[0015] The detected face image is preprocessed, including grayscale, normalization, cropping and enhancement operations, to optimize image quality and remove background information, and a valid face image is determined based on the tilt angle of the face.

[0016] Optionally, each captured facial image is subjected to facial recognition using a deep learning model, specifically including:

[0017] Analyze the captured facial images using a pre-trained deep learning model, where the deep learning model can detect 150 facial key points and locate the key points of the face, eyes, eyebrows, lips, and nose;

[0018] At the same time, it can accurately locate single and multiple faces, and use the face angle judgment function to analyze the faces in the picture at various posture angles, and obtain valid data based on the face space posture angle reference.

[0019] Optionally, the collected running data is processed and statistically analyzed in real time to generate intelligent evaluation results of the target to be tested, including:

[0020] Calculate basic data, including calculating the cumulative running mileage of students in a day or a period of time; calculating the cumulative running time of students in a day or a period of time; calculating the average running speed of students in a day or a period of time; generating daily and weekly rankings of the whole school based on students' running data;

[0021] Generate personalized sports analysis reports based on students' running data, including analyzing students' running performance in different time periods and judging their sports trends; provide personalized sports suggestions based on students' running data and sports trends;

[0022] Generate intelligent evaluation results of the target to be tested based on the basic data and motion analysis report of the target to be tested.

[0023] Optionally, the method further comprises:

[0024] The intelligent evaluation results are transmitted to the outdoor large screen, which provides a simple and intuitive user interface, supports multiple interactive methods, and displays the running student information and the daily / weekly rankings of the whole school. The running student information includes the student avatar who is currently running on the playground and today's running mileage. The daily / weekly rankings of the whole school include the student's name, class, total running mileage, and total duration information every day / week.

[0025] In a second aspect, a running intelligent evaluation system based on face recognition is provided, the system comprising:

[0026] The acquisition module includes installing intelligent acquisition devices at various monitoring points in the running area and capturing facial images through the intelligent acquisition devices; wherein the intelligent acquisition devices at least include a high-resolution camera and an edge computing device, the high-resolution camera is used to capture facial images, and the edge computing device is used to perform preliminary facial detection and preprocessing;

[0027] The recognition module is used to perform face recognition on each captured face image using a deep learning model, and compare it one by one with the pre-stored student face database to determine whether the student corresponding to each face image is the target to be tested. If the comparison is successful, the identity information of the successfully matched student is associated with the running record;

[0028] The processing module is used to capture the face of the students passing through each monitoring point during the running of the target to be tested, identify the target to be tested, and collect the running data of the target to be tested; wherein the running data includes running mileage, duration and pace data;

[0029] The generation module is used to perform real-time processing and statistical analysis on the collected running data to generate intelligent evaluation results for the target to be tested.

[0030] In a third aspect, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the running intelligent evaluation method described in any one of the first aspects is implemented.

[0031] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the running intelligent evaluation method described in any one of the first aspects is implemented.

[0032] In a fifth aspect, a computer program product is provided, including a computer program / instruction, which, when executed by a processor, implements any of the running intelligent evaluation methods described in the first aspect.

[0033] The beneficial effects brought by the technical solution provided by the embodiment of the present application include at least:

[0034] (1) By displaying students’ running data, daily rankings, and weekly rankings in real time, students’ competitive awareness and enthusiasm for sports are stimulated. Personalized exercise suggestions and real-time feedback mechanisms further enhance students’ sense of participation, encourage them to actively participate in physical exercise, and improve their physical fitness.

[0035] (2) Automatically collect, process and analyze running data, generate intelligent evaluation results, and provide scientific management basis for teachers and administrators. It reduces the workload of manual statistics and management, improves management efficiency and data accuracy, and enables schools to formulate physical education teaching plans and evaluate students' sports performance more scientifically. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0037] Figure 1 A flowchart of a running intelligent evaluation method based on face recognition provided in an embodiment of the present application;

[0038] Figure 2 A block diagram of a running intelligent evaluation system based on face recognition provided in an embodiment of the present application;

[0039] Figure 3 A schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0041] In the description of the present invention, the terms "comprises", "has" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may also include other steps or units that are not explicitly listed but are inherent to these processes, methods, products or apparatuses, or steps or units added based on further optimization schemes conceived by the present invention.

[0042] Please refer to Figure 1 , which shows a flow chart of a running intelligent evaluation method based on face recognition provided by an embodiment of the present application, which may include the following steps:

[0043] S1, install intelligent data collection equipment at each monitoring point in the running area, and capture facial images through the intelligent data collection equipment.

[0044] Among them, the intelligent collection equipment includes at least a high-resolution camera and an edge computing device. The high-resolution camera is used to capture faces, and the edge computing device is used to perform preliminary face detection and preprocessing.

[0045] In this step, intelligent data collection equipment is installed at various monitoring points in the running area of ​​the school playground. These devices include high-resolution cameras and built-in edge computing modules. The high-resolution camera is used to capture facial images in real time to ensure image clarity and quality, which can meet the needs of face recognition.

[0046] Intelligent collection equipment is connected via a gigabit wired network to ensure the stability and real-time performance of data transmission.

[0047] The edge computing module is responsible for the preliminary processing of the captured facial images, including face detection, image preprocessing (such as grayscale, normalization, cropping and enhancement), and face angle judgment, reducing the amount of data transmission and improving the system response speed.

[0048] When students enter the running area, the intelligent acquisition device automatically starts the capture process, captures the students' facial images, and performs preliminary processing to prepare for subsequent facial recognition.

[0049] In an optional embodiment of the present application, an effective capture time is set. When the intelligent acquisition device is running, it will obtain the current time in real time and compare it with the set effective capture time. When entering the running area within the effective capture time, the intelligent acquisition device will start the capture process;

[0050] Set a minimum number of captured photos. During the capture process, the intelligent collection device will count the photos captured by each student. When processing the captured data, it will check whether the number of captured photos of each student reaches the set minimum threshold. Only when the number of captured photos reaches or exceeds the set minimum number, the student's running data will be counted and recorded in the database.

[0051] S2, uses a deep learning model to perform face recognition on each captured face image, and compares it one by one with a pre-stored student face database to determine whether the student corresponding to each face image is the target to be tested. If the comparison is successful, the identity information of the successfully matched student will be associated with the running record.

[0052] In this step, the intelligent acquisition device transmits the preliminarily processed facial image to the back-end system through the network.

[0053] The face recognition engine in the back-end system uses a deep learning model to perform high-precision recognition of captured facial images.

[0054] This deep learning model is based on large-scale data training and can detect 150 key points, accurately locate facial features (such as eyebrows, eyes, nose, lips, cheeks, etc.) and contours, and support recognition of single and multiple faces.

[0055] The system makes angle judgments on facial images and combines them with the spatial posture angle reference of the face to enhance the accuracy and robustness of recognition.

[0056] During the recognition process, the system will compare the captured facial image with the pre-stored student face database one by one to determine whether the student is a student of this school (i.e., the target to be tested).

[0057] If the match is successful, the system will associate the student's identity information with the running record, providing a basis for subsequent data statistics and analysis.

[0058] S3, when the target to be measured is running, the face of the students passing through each monitoring point is captured to confirm the target to be measured, and the running data of the target to be measured is collected.

[0059] Among them, running data includes running mileage, duration and pace data.

[0060] In this step, while the target to be measured (confirmed student) is running, the intelligent data collection device continuously captures the face of the student passing through each monitoring point.

[0061] The system uses facial comparison to confirm whether the captured student is the target to be tested, ensuring the accuracy of the data.

[0062] After each successful capture, the system records the time when the student passes the monitoring point and calculates running mileage, duration, pace and other data based on chronological order.

[0063] The running mileage is obtained by calculating the length of the path the students pass through the monitoring points; the running time is obtained by recording the difference between the time when the students start running and the time when they end running; and the pace is calculated by dividing the running mileage by the running time.

[0064] The system transmits the collected running data to the back-end system in real time for storage and further processing.

[0065] In an optional embodiment of the present application, human body recognition is also included:

[0066] When the target is running, the intelligent data acquisition device not only captures the face, but also starts the body recognition function at the same time. Body recognition technology can detect a single or multiple people in the picture, track the people, and feedback the relative position of the people in the picture.

[0067] The human body recognition algorithm can detect the student's body outline in real time and track it. Even when the face is partially blocked (such as wearing a hat, mask, etc.) or the face cannot be captured clearly due to angle problems, the human body recognition technology can still confirm the student's identity and location through the body outline and movement trajectory.

[0068] Human body recognition technology can provide information about the relative position of the human body in the image, helping the system to more accurately determine whether students have passed through monitoring points and their specific positions during running.

[0069] During the face capture process, human body recognition technology can assist in confirming whether the captured face belongs to a student who is running, avoiding data errors caused by mistakenly capturing other non-running people.

[0070] In a scenario where multiple people are running, human body recognition technology can distinguish different students and ensure that each student's running data is accurately collected. Even if there is overlap between students or the queues are tight, human body recognition technology can still accurately distinguish them through body contours and movement trajectories.

[0071] The location information provided by human body recognition technology can more accurately calculate the student's running mileage and pace. For example, the system can more accurately estimate the running path and speed based on the movement trajectory of the human body between monitoring points.

[0072] During running, human body recognition technology can provide real-time feedback on students' exercise status, helping the system to adjust data collection strategies in a timely manner to ensure the accuracy and completeness of the data.

[0073] S4, performs real-time processing and statistical analysis on the collected running data to generate intelligent evaluation results of the target to be tested.

[0074] In this step, the back-end system processes the received running data in real time, including data cleaning, verification and integration, to ensure the accuracy and completeness of the data.

[0075] The system performs statistical analysis on the running data of each target to be tested and calculates basic statistical indicators such as total mileage, total time, and average pace.

[0076] Based on the statistical results, daily and weekly rankings for the entire school are generated to display the students' rankings and encourage students to actively participate in running exercises.

[0077] The system also performs personalized analysis, such as exercise trend analysis and exercise suggestion generation, to provide targeted exercise guidance for each student.

[0078] Ultimately, the system generates intelligent evaluation results, including students' running data, ranking information, sports trend analysis, and personalized recommendations.

[0079] The intelligent evaluation results are displayed to students and administrators in real time through a user-friendly interface (such as a large sunlight screen), and can also be sent to relevant personnel in the form of electronic reports, providing a scientific basis for the school's physical education and management. The user-friendly interface supports a variety of interactive methods, and displays running student information and the school's daily / weekly leaderboard; the running student information includes the student's head portrait and today's running mileage, and the school's daily / weekly leaderboard includes the student's name, class, total running mileage, and total duration information for each day / week.

[0080] Please refer to Figure 2 , which shows a block diagram of a running intelligent evaluation system based on face recognition provided by an embodiment of the present application. Figure 2 As shown, the system may include:

[0081] The acquisition module includes installing intelligent acquisition devices at various monitoring points in the running area and capturing facial images through the intelligent acquisition devices; wherein the intelligent acquisition devices at least include a high-resolution camera and an edge computing device, the high-resolution camera is used to capture facial images, and the edge computing device is used to perform preliminary facial detection and preprocessing;

[0082] The recognition module is used to perform face recognition on each captured face image using a deep learning model, and compare it one by one with the pre-stored student face database to determine whether the student corresponding to each face image is the target to be tested. If the comparison is successful, the identity information of the successfully matched student is associated with the running record;

[0083] The processing module is used to capture the face of the students passing through each monitoring point during the running of the target to be tested, identify the target to be tested, and collect the running data of the target to be tested; wherein the running data includes running mileage, duration and pace data;

[0084] The generation module is used to perform real-time processing and statistical analysis on the collected running data to generate intelligent evaluation results for the target to be tested.

[0085] For the specific definition of the running intelligent evaluation system based on face recognition, please refer to the definition of the running intelligent evaluation method based on face recognition above, which will not be repeated here. Each module in the above-mentioned running intelligent evaluation system based on face recognition can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0086] In one embodiment, an electronic device is provided. The electronic device may be a computer, and its internal structure diagram may be as follows: Figure 3 As shown. The electronic device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the device is used to provide computing and control capabilities. The memory of the device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used for running intelligent evaluation data based on face recognition. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a running intelligent evaluation method based on face recognition is implemented.

[0087] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0088] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which involves all or part of the processes in the above-mentioned embodiment method.

[0089] In one embodiment, a computer program product is also provided, including a computer program / instruction, which involves all or part of the process in the above embodiment method.

[0090] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in M ​​forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SyMchliMk) DRAM (SLDRAM), memory bus (RaMbus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0091] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0092] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the patent application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent application shall be subject to the attached claims.

Claims

1. A running intelligent evaluation method based on face recognition, characterized in that: The method comprises: Install intelligent acquisition equipment at each monitoring point in the running area, and capture facial images through the intelligent acquisition equipment; wherein the intelligent acquisition equipment at least includes a high-resolution camera and an edge computing device, the high-resolution camera is used to capture the face, and the edge computing device is used to perform preliminary face detection and preprocessing; Each captured face image is recognized using a deep learning model, and is compared one by one with a pre-stored student face database to determine whether the student corresponding to each face image is the target to be tested. If the comparison is successful, the identity information of the successfully matched student is associated with the running record; When the target is running, the face of the student passing through each monitoring point is captured to confirm the target, and the running data of the target is collected; the running data includes running mileage, duration and pace data; The collected running data is processed and statistically analyzed in real time to generate intelligent evaluation results for the target to be tested.

2. The running intelligent evaluation method according to claim 1, characterized in that: Install intelligent data collection equipment at various monitoring points in the running area, including: Set the effective capture time. When the smart acquisition device is running, it will obtain the current time in real time and compare it with the set effective capture time. The smart acquisition device will start the capture process only when you enter the running area within the effective capture time. Set a minimum number of captured photos. During the capture process, the intelligent collection device will count the photos captured by each student. When processing the captured data, it will check whether the number of captured photos of each student reaches the set minimum threshold. Only when the number of captured photos reaches or exceeds the set minimum number, the student's running data will be counted and recorded in the database.

3. The running intelligent evaluation method according to claim 1, characterized in that: The edge computing device performs preliminary face detection and preprocessing, including: After capturing an image through a high-resolution camera, the NPU algorithm is used to identify the face area in the image and determine its position and size; The detected face image is preprocessed, including grayscale, normalization, cropping and enhancement operations, to optimize image quality and remove background information, and a valid face image is determined based on the tilt angle of the face.

4. The running intelligent evaluation method according to claim 1, characterized in that: Each captured facial image is used for face recognition using a deep learning model, including: Analyze the captured facial images using a pre-trained deep learning model, where the deep learning model can detect 150 facial key points and locate the key points of the face, eyes, eyebrows, lips, and nose; At the same time, it can accurately locate single and multiple faces, and use the face angle judgment function to analyze the faces in the picture at various posture angles, and obtain valid data based on the face space posture angle reference.

5. The running intelligent evaluation method according to claim 1, characterized in that: The collected running data is processed and statistically analyzed in real time to generate intelligent evaluation results of the target to be tested, including: Calculate basic data, including calculating the cumulative running mileage of students in a day or a period of time; calculating the cumulative running time of students in a day or a period of time; calculating the average running speed of students in a day or a period of time; generating daily and weekly rankings of the whole school based on students' running data; Generate personalized sports analysis reports based on students' running data, including analyzing students' running performance in different time periods and judging their sports trends; provide personalized sports suggestions based on students' running data and sports trends; Generate intelligent evaluation results of the target to be tested based on the basic data and motion analysis report of the target to be tested.

6. The running intelligent evaluation method according to claim 1, characterized in that: The method further comprises: The intelligent evaluation results are transmitted to the outdoor large screen, which provides a simple and intuitive user interface, supports multiple interactive methods, and displays the running student information and the daily / weekly rankings of the whole school. The running student information includes the student avatar who is currently running on the playground and today's running mileage. The daily / weekly rankings of the whole school include the student's name, class, total running mileage, and total duration information every day / week.

7. An intelligent running evaluation system based on face recognition, characterized in that: The system comprises: The acquisition module includes installing intelligent acquisition devices at various monitoring points in the running area and capturing facial images through the intelligent acquisition devices; wherein the intelligent acquisition devices at least include a high-resolution camera and an edge computing device, the high-resolution camera is used to capture facial images, and the edge computing device is used to perform preliminary facial detection and preprocessing; The recognition module is used to perform face recognition on each captured face image using a deep learning model, and compare it one by one with the pre-stored student face database to determine whether the student corresponding to each face image is the target to be tested. If the comparison is successful, the identity information of the successfully matched student is associated with the running record; The processing module is used to capture the face of the students passing through each monitoring point during the running of the target to be tested, identify the target to be tested, and collect the running data of the target to be tested; wherein the running data includes running mileage, duration and pace data; The generation module is used to perform real-time processing and statistical analysis on the collected running data to generate intelligent evaluation results for the target to be tested.

8. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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