Visitor identity automatic verification method and system based on intelligent access control

Abstract

The application discloses a visitor identity automatic verification method and system based on intelligent access control, belongs to the technical field of access control and identity verification, and comprises the following steps: an access control monitoring camera senses whether a visitor appears in a scanning range in real time; when the visitor is sensed to appear, image information scanned by the camera at the moment is acquired; and an image external interference execution scheme is analyzed. A stripe jitter characteristic factor is analyzed, and an image stripe jitter execution scheme is analyzed. Stripe jitter adjustment is performed, a stripe jitter characteristic factor at the moment is analyzed, stripe jitter adjustment effect information is analyzed, and an image stripe jitter adjustment effect execution scheme is obtained. A coupling interference characteristic factor is analyzed, a coupling interference execution scheme is analyzed, and coupling interference adjustment is performed. The visitor identity automatic verification is executed based on a YOLOv5s target detection algorithm. The application analyzes and adjusts stripe jitter to guarantee camera imaging quality and improve recognition accuracy, analyzes and adjusts coupling interference, eliminates coupling interference, and improves image quality.

Description

Technical Field

[0001] The present invention relates to the technical field of access control identity verification, in particular to an automatic visitor identity verification method and system based on intelligent access control. Background Art

[0002] The existing automatic intelligent verification technologies for visitor identities mainly include multi-modal biometric technologies, intelligent hardware terminals, cloud and data management. First, face recognition and live detection are performed through multi-modal biometric technologies. The visitors are identified through cameras for identity verification to ensure the accuracy of the recognition judgment. Through the intelligent visitor management terminal, self-service registration and mobile terminal collaborative management are carried out, and the visitor information data is synchronized to the platform in real time. Cloud and edge computing collaboration is carried out, and an AI risk decision engine is used to judge abnormal visitors. Multi-camera linkage scanning is used to analyze abnormal behaviors. A multi-dimensional and intelligent technical system is formed, combined with AI algorithms and cloud management, to achieve efficient, accurate and safe identity verification. [[ID=⑨]] [[ID=⑩]]

[0003] [[ID=⑪]]For example, a method and system for household safety control in a smart community disclosed in a Chinese invention patent with the publication number CN117273444B controls the risk behaviors of community households by changing the form of risk attention items. Since the dissimilarity between the interval binary groups in the safety hazard data to be analyzed and the corresponding potential safety hazard data is large enough, it means that there is a risk attention item in this interval binary group, which is an interval binary group related to the risk attention item. Therefore, this interval binary group is determined as the target interval binary group, and according to the commonality weight of the knowledge fields of the target interval binary group, the changed form of the risk attention item in the target interval binary group can be determined. [[ID=⑫]] [[ID=⑬]]

[0004] [[ID=⑭]]For example, a smart monitoring system for a community disclosed in a Chinese invention patent with the publication number CN109711769B relates to the field of community monitoring. Specifically, it relates to a smart monitoring system for a community, including: a delivery information generation module for obtaining the owner's request information to generate a delivery information table, and the delivery information table includes: the owner's personal information, delivery address, delivery time, the name, gender, contact information and affiliated company of the food delivery person. A preferred route module for obtaining the community map information and the delivery address in the delivery information table to obtain the delivery route, and selecting the best delivery route from the delivery routes through the analysis of the delivery time and the delivery floor in the delivery address. A safety warning module, when the delivery duration exceeds the safety duration, it is judged as abnormal delivery, a reminder signal is sent to the food delivery person, and at the same time a safety warning signal is sent to the security personnel. [[ID=⑮]]<~ [[ID=⑯]]

[0005] [[ID=⑰]]The above technologies at least have the following technical problems: [[ID=⑱]] [[ID=⑲]]

[0006] The current visitor identity automatic verification technology for intelligent access control mainly focuses on analyzing the behavior of the entering visitor, and monitors the visitor access route through multiple monitoring devices, but ignores the influence of the monitoring and verification equipment on the monitoring imaging due to weather reasons. Incompatible frequencies may cause bright and dark stripes in the image, which seriously affects the picture quality collected by the camera, and then interferes with the accuracy of the face recognition visual algorithm. The coupling effect of multiple problems will cause greater interference, interfere with the camera imaging, affect the image quality, and reduce the accuracy of verification and recognition. SUMMARY

[0007] In order to solve the above technical problems existing in the prior art, the embodiments of the present application provide a visitor identity automatic verification method and system based on intelligent access control. The technical scheme is as follows:

[0008] On the one hand, a visitor identity automatic verification method based on intelligent access control is provided, comprising the following steps:

[0009] The access control monitoring camera senses in real time whether a visitor appears in the scanning range. When a visitor is sensed to appear, the image information scanned by the camera at this time is obtained, the dark channel mean of the image is obtained, the image external interference execution scheme is analyzed, and the all-weather reliability of identification is enhanced to cope with complex scenes.

[0010] When the image external interference execution scheme is for image information analysis, the stripe jitter characteristic factor is analyzed, the image stripe jitter execution scheme is analyzed, the picture quality collected by the camera is improved, and the recognition accuracy is improved.

[0011] When the image stripe jitter execution scheme is for stripe jitter adjustment, the stripe jitter characteristic factor deviation value is analyzed synchronously, stripe jitter adjustment is performed, after the stripe jitter adjustment, the stripe jitter characteristic factor at this time is analyzed, the stripe jitter adjustment effect information is analyzed, and thus the image stripe jitter adjustment effect execution scheme is obtained to ensure the camera imaging quality and eliminate the recognition interference.

[0012] When the image stripe jitter adjustment effect execution scheme is for coupling interference judgment, the coupling interference characteristic factor is analyzed, the coupling interference execution scheme is analyzed, the coupling interference adjustment is performed, the coupling interference is eliminated, and the image quality is improved.

[0013] When the image external interference execution scheme is for continuing visitor scanning verification, or the image stripe jitter execution scheme is for continuing visitor scanning verification, or the image stripe jitter adjustment effect execution scheme is for continuing visitor scanning verification, the visitor identity automatic verification is performed based on the YOLOv5s target detection algorithm.

[0014] On the other hand, a visitor identity automatic verification system based on intelligent access control is provided, comprising:

[0015] The outside interference judgment module judges whether a visitor appears in the scanning range in real time, and obtains image information scanned by the camera when the visitor appears.

[0016] The stripe jitter judgment module analyzes the stripe jitter characteristic factor when the image outside interference execution scheme is image information analysis, analyzes the image stripe jitter execution scheme, improves the picture quality collected by the camera, and improves the recognition accuracy.

[0017] The stripe jitter adjustment module synchronously analyzes the stripe jitter characteristic factor deviation value when the image stripe jitter execution scheme is stripe jitter adjustment, adjusts the stripe jitter, analyzes the stripe jitter characteristic factor after the stripe jitter adjustment, analyzes the stripe jitter adjustment effect information, and thus obtains the image stripe jitter adjustment effect execution scheme, so as to guarantee the camera imaging quality and eliminate the recognition interference.

[0018] The coupling interference analysis module analyzes the coupling interference characteristic factor when the image stripe jitter adjustment effect execution scheme is coupling interference judgment, analyzes the coupling interference execution scheme, adjusts the coupling interference, eliminates the coupling interference, and improves the image quality.

[0019] The visitor identity verification module performs automatic visitor identity verification based on the YOLOv5s target detection algorithm when the image outside interference execution scheme is to continue visitor scanning verification, or the image stripe jitter execution scheme is to continue visitor scanning verification, or the image stripe jitter adjustment effect execution scheme is to continue visitor scanning verification.

[0020] The technical scheme provided by the embodiment of the application has at least the following beneficial effects:

[0021] 1. The visitor identity automatic verification method and system based on intelligent access control provided by the application can avoid picture distortion, guarantee smooth output pictures, eliminate misjudgment interference by adjusting the jitter, improve the obtained image quality, prevent equipment overload by dynamically analyzing and adjusting the stripe jitter, dynamically adjust the power consumption, analyze and adjust the coupling problem, improve the recognition rate in the coupling interference, optimize the verification efficiency, and improve the verification accuracy.

[0022] 2. The application adjusts the frequency and duty cycle based on the stripe jitter characteristic factor deviation value by adjusting the stripe jitter, solves the problem of image stripe jitter, improves the picture quality collected by the camera, improves the recognition accuracy, has robustness in different scenes, optimizes the image quality, and improves the accuracy of the access visitor automatic verification.

[0023] 3、The application obtains an image stripe jitter adjustment effect execution scheme by analyzing stripe jitter adjustment effect information, judges the stripe jitter adjustment effect information based on a first stripe jitter representation factor and a stripe jitter representation factor correction threshold, judges the adjustment effect with a more stringent standard, can more accurately measure the effectiveness of the adjustment operation, for effective stripe jitter adjustment, as much as possible to reduce consumption, avoid introducing other interference, no longer adjust, continue to scan and verify the visitor, for invalid stripe jitter adjustment, judge the coupling interference, solve the interference problem, to protect the camera imaging quality, eliminate the recognition interference.

[0024] 4、The application adjusts the frequency and light intensity based on the coupling interference representation factor deviation value to eliminate coupling interference, improve system reliability, reduce recognition misjudgment rate, make recognition more accurate, improve image quality, effectively solve the coupling interference problem, accurately control signal timing, eliminate the periodic characteristics of the interference signal, accurately destroy the physical interference mechanism, break the resonance interference, use high-frequency suppression to suppress visible jitter, can suppress the amplitude domain influence of scattered noise, and guarantee the quality of the obtained image. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0026] Figure 1 It is a visitor identity automatic verification method flowchart based on intelligent access control provided by the embodiment of the application.

[0027] Figure 2 It is a visitor identity automatic verification system structure schematic diagram based on intelligent access control provided by the embodiment of the application.

[0028] Figure 3 It is an analysis image stripe jitter execution scheme flowchart related to the embodiment of the application.

[0029] Figure 4 It is an analysis stripe jitter adjustment effect information flowchart related to the embodiment of the application. DETAILED DESCRIPTION

[0030] The technical solutions in the application will be described below with reference to the drawings.

[0031] In the embodiments of the present application, the words such as "exemplary", "for example", etc. are used to represent an example, illustration, or description. Any embodiment or design scheme described as "exemplary" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the word "exemplary" is intended to present the concept in a specific manner. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or one of the two.

[0032] In the embodiments of the present application, "image" and "picture" can be used interchangeably at times. It should be pointed out that when the distinction is not emphasized, the meanings expressed are consistent. "Of", "corresponding" and "corresponding" can be used interchangeably at times. It should be pointed out that when the distinction is not emphasized, the meanings expressed are consistent.

[0033] In the embodiments of the present application, sometimes the subscript such as W1 can be written in the form of non-subscript such as W1. When the distinction is not emphasized, the meanings expressed are consistent.

[0034] In order to make the technical problems, technical schemes and advantages to be solved by the present application more clear, the following will be described in detail in conjunction with the drawings and specific embodiments.

[0035] The embodiments of the present application provide a visitor identity automatic verification method based on intelligent access control, as shown in Figure 1 The flow chart of the visitor identity automatic verification method based on intelligent access control, the method comprises:

[0036] The intelligent access control involved in the present embodiment can specifically include but is not limited to community access control, school access control, hospital access control and bank access control, etc.

[0037] The access control monitoring camera can perceive in real time whether a visitor appears in the scanning range. When it is perceived that a visitor appears, the image information scanned by the camera at this time is acquired, the dark channel mean of the image is acquired, the image external interference execution scheme is analyzed, and the all-weather reliability of identification is enhanced to cope with complex scenes.

[0038] The access control monitoring camera can perceive in real time whether a visitor appears in the scanning range. When it is perceived that a visitor appears, the image information scanned by the camera at this time is acquired, the dark channel mean of the image is acquired, the image external interference execution scheme is analyzed, and the all-weather reliability of identification is enhanced to cope with complex scenes.

[0039] When the infrared pyroelectric sensor detects infrared heat radiation, it is determined that a visitor appears.

[0040] In the present embodiment, the dark channel mean of the image is acquired, and the image external interference execution scheme is analyzed. The analysis process is as follows:

[0041] Obtain the mean value of the dark channel of the image.

[0042] The dark channel mean is the arithmetic mean of all pixel values ​​in a dark channel image. The dark channel image is generated as follows: for each pixel in the input RGB image, the minimum value of its red, green, and blue channel intensity values ​​is first taken. Then, within a preset local window in the database, the local minimum value of these minimum values ​​is taken to form a single-channel image. This single channel is not a regular grayscale image, but rather a mapping reflecting the characteristics of the darkest pixels in a local area. The range of the dark channel mean depends on the image data type, commonly ranging from 0–255 or a normalized 0–1.

[0043] The dark channel mean of an image can be obtained using OpenCV + NumPy (NumericalPython, a numerical computation extension library).

[0044] Extract the dark channel mean threshold.

[0045] If the mean value of the dark channel of the image is greater than the threshold value of the dark channel, the method for handling external interference to the image is recorded as performing image information analysis.

[0046] It should be noted that if the dark channel mean of the image is greater than the dark channel mean threshold, it indicates that the dark pixel density in the image is insufficient, suggesting that the image deviates from the normal statistical pattern and is affected by external interference. Further analysis is needed to determine whether a problem has occurred and to make targeted adjustments. Therefore, the image external interference execution scheme is denoted as image information analysis.

[0047] If the dark channel mean of the image is less than or equal to the dark channel mean threshold, the external interference of the image is recorded as continuing to perform visitor scanning verification.

[0048] It should be noted that if the dark channel mean of the image is less than or equal to the dark channel mean threshold, it means that the external environment is not interfering with the monitoring camera's verification of visitor identity. In order to reduce the complexity of the algorithm, reduce system power consumption, and prevent the introduction of other interference, no further adjustments will be made, and the execution scheme for external image interference will be recorded as continuing to scan and verify visitors.

[0049] When performing image information analysis, the external interference execution scheme analyzes the stripe jitter characterization factor and the image stripe jitter execution scheme to improve the image quality captured by the camera and increase recognition accuracy.

[0050] See Figure 3As shown, the flow chart of the image stripe jitter execution scheme is related to the embodiment of the present application, the stripe jitter characteristic parameters are collected, the stripe jitter characteristic factors are analyzed, the stripe jitter characteristic factor threshold is extracted, if the stripe jitter characteristic factor is greater than the stripe jitter characteristic factor threshold, the image stripe jitter execution scheme is recorded as stripe jitter adjustment, and if the stripe jitter characteristic factor is less than or equal to the stripe jitter characteristic factor threshold, the image stripe jitter execution scheme is recorded as continuing to perform visitor scanning verification.

[0051] In the embodiment, the stripe jitter characteristic factor is analyzed, and the analysis process is as follows:

[0052] The stripe jitter characteristic parameters are collected, including the stripe modulation depth of the current frame image, the frequency domain main peak energy ratio and the stripe coverage rate.

[0053] The current frame image refers to the frame image when the monitoring camera senses the presence of the visitor and analyzes the image information.

[0054] It should be noted that the stripe modulation depth is the intensity of the light and dark contrast of the stripe, which is the extreme value of the maximum gray value of the image single row of pixels and the minimum gray value of the image single row of pixels, that is, the ratio of the maximum gray value of the image single row of pixels minus the minimum gray value of the image single row of pixels to the maximum gray value of the image single row of pixels plus the minimum gray value of the image single row of pixels. The frequency domain main peak energy ratio is used to quantify the periodicity of the stripe, that is, the ratio of the main peak amplitude in the Fourier spectrum to the average energy of the spectrum. The stripe coverage rate refers to the spatial distribution proportion of the stripe in the image, that is, the ratio of the number of stripe pixels to the total number of pixels, wherein the stripe pixel is the area occupied by the stripe row.

[0055] When the stripe modulation depth increases, that is, the contrast of the stripe is enhanced, the main peak amplitude in the Fourier spectrum significantly increases, so that the frequency domain main peak energy ratio is enhanced, and high stripe modulation depth and high frequency domain main peak energy ratio will make the stripe spread in space, and then make the stripe coverage rate increase.

[0056] It should be noted that the stripe modulation depth and the stripe coverage rate can be obtained by Python, and the frequency domain main peak energy ratio can be obtained by OpenCV.

[0057] The stripe jitter characteristic factor is analyzed based on the stripe jitter characteristic parameters.

[0058] The stripe modulation depth, the frequency domain main peak energy ratio and the stripe coverage rate are compared with the corresponding reference values respectively, the comparison results are coupled and weighted by combining the corresponding measurement coefficients, so as to obtain the stripe jitter characteristic factor.

[0059] The stripe jitter characteristic factor is the quantitative representation of the stripe jitter state of the image by the stripe modulation depth, the frequency domain main peak energy ratio and the stripe coverage rate of the current frame image.

[0060] It should be noted that the reference fringe modulation depth, the reference frequency domain main peak energy ratio and the reference fringe coverage stored in the database are extracted.

[0061] The fringe modulation depth metric coefficient, the frequency domain main peak energy ratio metric coefficient and the fringe coverage metric coefficient preset in the database are extracted.

[0062] It should be noted that in the embodiment, in order to implement the weighted coupling of the fringe jitter characterization factor, the database is pre-constructed with a mapping relationship of multiple groups of parameters, which is used to associate different fringe modulation depths, frequency domain main peak energy ratios and fringe coverages with their corresponding metric coefficients. The mapping relationship is stored in the form of a structured configuration data table (for example, a metric configuration table). The configuration table defines the metric coefficient values corresponding to different fringe modulation depths, frequency domain main peak energy ratios and fringe coverages. The above configuration table is combined with the fringe jitter characterization parameters through conditional matching to implement dynamic adaptation of the fringe jitter characterization factor. Based on the configuration table, the system can automatically extract the metric coefficients matched with the fringe jitter characterization factor for subsequent weighted coupling calculation. The value range of each metric coefficient is limited to 0 to 1, and the sum of the three is 1, so as to ensure that the multi-parameter fusion result has the properties of normalization and physical consistency, and meets the weight constraint conditions required by the fringe jitter analysis.

[0063] In specific embodiments, the fringe jitter characterization factor is specifically represented as follows:

[0064]

[0065] Wherein, L is the fringe jitter characterization factor of the current frame image, T is the fringe modulation depth of the current frame image, P is the frequency domain main peak energy ratio of the current frame image, U is the fringe coverage of the current frame image, T vef is the reference fringe modulation depth, P vef is the reference frequency domain main peak energy ratio, U vef is the reference fringe coverage, T1 is the fringe modulation depth metric coefficient, P1 is the frequency domain main peak energy ratio metric coefficient, and U1 is the fringe coverage metric coefficient.

[0066] In the embodiment, the image fringe jitter execution scheme is analyzed, and the analysis process is as follows:

[0067] The fringe jitter characterization factor threshold is extracted.

[0068] If the fringe jitter characterization factor is greater than the fringe jitter characterization factor threshold, the image fringe jitter execution scheme is recorded as performing fringe jitter adjustment.

[0069] ​It should be noted that the stripe jitter characteristic factor is greater than the stripe jitter characteristic factor threshold value, which indicates that the image obtained at this time has a stripe jitter problem, which can cause the image to have wavy interference or flicker, reduce the image quality, and easily cause visitor visual fatigue. In order to eliminate misjudgment interference and avoid picture distortion, the image stripe jitter execution scheme is recorded as stripe jitter adjustment.

[0070] If the stripe jitter characteristic factor is less than or equal to the stripe jitter characteristic factor threshold value, the image stripe jitter execution scheme is recorded as continuing visitor scanning verification.

[0071] The YOLOv5s target detection algorithm is used to continue visitor scanning verification.

[0072] It should be noted that the stripe jitter characteristic factor is less than or equal to the stripe jitter characteristic factor threshold value, which indicates that the image does not have a stripe jitter problem at this time, although there may be external interference, but it does not reduce the image quality, nor does it interfere with visual recognition. In order to reduce the power consumption, the image stripe jitter execution scheme is recorded as continuing visitor scanning verification.

[0073] When the image stripe jitter execution scheme is stripe jitter adjustment, the stripe jitter characteristic factor deviation value is analyzed synchronously, the stripe jitter adjustment is performed, and after the stripe jitter adjustment, the stripe jitter characteristic factor at this time is analyzed, the stripe jitter adjustment effect information is analyzed, and thus the image stripe jitter adjustment effect execution scheme is obtained, so as to guarantee the camera imaging quality and eliminate the recognition interference.

[0074] Referring to Figure 4 The first stripe jitter characteristic factor is obtained, a stripe jitter characteristic factor threshold correction coefficient is extracted, a stripe jitter characteristic factor correction threshold value is obtained based on the stripe jitter characteristic factor threshold correction coefficient and the stripe jitter characteristic factor threshold value, if the first stripe jitter characteristic factor is greater than the stripe jitter characteristic factor correction threshold value, the stripe jitter adjustment effect information is invalid adjustment, the image stripe jitter adjustment effect execution scheme is recorded as coupling interference judgment, and if the first stripe jitter characteristic factor is less than or equal to the stripe jitter characteristic factor correction threshold value, the stripe jitter adjustment effect information is effective adjustment, and the image stripe jitter adjustment effect execution scheme is recorded as continuing visitor scanning verification.

[0075] In this embodiment, the stripe jitter adjustment is performed, and the specific process is as follows:

[0076] The stripe jitter characteristic factor and the stripe jitter characteristic factor threshold value are subjected to difference processing to obtain the stripe jitter characteristic factor deviation value.

[0077] It should be noted that the stripe jitter characterization factor deviation value is obtained by subtracting the stripe jitter characterization factor threshold value from the stripe jitter characterization factor.

[0078] The frequency adjustment value and the duty cycle adjustment value are extracted based on the stripe jitter characterization factor deviation value.

[0079] It should be noted that the frequency adjustment value and the duty cycle adjustment value are numerical data, and have no positive or negative meaning.

[0080] In this embodiment, the system pre-constructs the mapping relationship between the stripe jitter characterization factor deviation value and the corresponding frequency adjustment value and duty cycle adjustment value in the database, and the mapping relationship is managed in the form of a structured configuration table (for example, a stripe jitter parameter configuration table). In the matching process, the system retrieves the corresponding frequency adjustment value and duty cycle adjustment value in the configuration table according to the stripe jitter characterization factor deviation value, taking the deviation value as a query key. By finding the frequency adjustment value and the duty cycle adjustment value that match the stripe jitter characterization factor deviation value, the system can dynamically adjust the stripe jitter, thereby ensuring the quality of the obtained image.

[0081] It should be noted that the greater the stripe jitter characterization factor deviation value, the more serious the stripe jitter problem at this time, which will cause the loss of key content during scanning, and will cause the recognition error rate to rise. Severe stripe jitter will reduce user comfort and easily cause the device to be overloaded and damaged. In order to obtain complete image information and improve the accuracy of visual recognition, the greater the corresponding extracted frequency adjustment value and duty cycle adjustment value, the greater the adjustment of the stripe jitter, the greater the picture quality of the camera acquisition, and the higher the recognition accuracy.

[0082] Adjusting the frequency and the duty cycle can effectively solve the stripe jitter problem and optimize the image effect. The core is to eliminate the periodic characteristics of the interference signal by precisely controlling the signal timing and energy distribution. The frequency adjustment can break the resonance interference, and the use of high-frequency suppression can suppress visible jitter. The duty cycle adjustment makes the energy distribution uniform, and eliminates the sharp edge stripe. Therefore, adjusting the frequency and the duty cycle can solve the stripe jitter problem and ensure the quality of the obtained image.

[0083] The rolling shutter row period of the camera is obtained.

[0084] It should be noted that the rolling shutter row period of the camera is a period that the camera device itself has, and is directly obtained from the database when used.

[0085] The light frequency is supplemented based on the rolling shutter row period of the camera and the frequency adjustment value.

[0086] It should be noted that the supplemental adjustment of the dimming frequency refers to multiplying the frequency adjustment value by the rolling shutter row period of the camera to obtain an integer multiple of one of the rolling shutter row period of the camera. In one specific embodiment, assuming that the rolling shutter row period of the camera is 10 μs and the frequency adjustment value is 1, the execution value of the supplemental adjustment of the dimming frequency is 100 kHz.

[0087] Multiplying the frequency adjustment value by the rolling shutter row period of the camera to obtain an integer multiple of one of the rolling shutter row period of the camera, the camera sensor is exposed row by row from top to bottom, and the exposure start time of each row is different by a row period. If the frequency is not synchronized with the row period, the phases of the signals captured in the integration time of each row are different, which will cause the brightness of each row to be inconsistent, and then cause the image to have bright and dark stripes, resulting in stripe jitter. The integer multiple relationship is to perform phase alignment to ensure that the exposure window of each row covers the same number of complete periods, so that the stripe suppression effect is best and the brightness difference between rows is eliminated.

[0088] Obtain the duty cycle of the dimming light source at this time.

[0089] It should be noted that the duty cycle of the dimming light source refers to the proportion of the light source power-on time to the total period in a complete period, which directly determines the visual brightness and stroboscopic characteristics of the light source.

[0090] The duty cycle of the dimming light source can be directly obtained through Arduino.

[0091] Based on the duty cycle adjustment value and the duty cycle of the dimming light source at this time, the duty cycle is supplemented and adjusted.

[0092] It should be noted that the value obtained by adding the duty cycle adjustment value and the duty cycle of the dimming light source at this time is used as the execution value of the supplemental adjustment of the duty cycle.

[0093] In this embodiment, the stripe jitter adjustment effect information is analyzed, and thus the image stripe jitter adjustment effect execution scheme is obtained, and the analysis process is as follows:

[0094] Obtain the stripe jitter representation factor at this time, denoted as the first stripe jitter representation factor.

[0095] Based on the frequency adjustment value, the stripe jitter representation factor threshold correction coefficient is extracted.

[0096] It should be noted that the system previously constructs the corresponding relationship between the frequency adjustment value and the corresponding stripe jitter characteristic factor threshold correction coefficient in the database, and the corresponding relationship is managed by a structured data table (such as a stripe jitter correction coefficient table). In the matching process, the system uses the frequency adjustment value as the search key value to query the corresponding stripe jitter characteristic factor threshold correction coefficient. The correction coefficient is used to dynamically adjust the stripe jitter, thereby optimizing the application of the stripe jitter threshold and improving the precision and stability of the obtained image.

[0097] It should be noted that the greater the frequency adjustment value, the more serious the stripe jitter problem, the poorer the quality of the obtained image, and the greater the degree of stripe jitter adjustment. In order to ensure the imaging quality of the camera and eliminate identification interference, the stripe jitter characteristic factor threshold is strictly corrected, and therefore the corresponding extracted stripe jitter characteristic factor threshold correction coefficient is greater.

[0098] The stripe jitter characteristic factor correction threshold is obtained based on the stripe jitter characteristic factor threshold and the stripe jitter characteristic factor threshold correction coefficient.

[0099] It should be noted that the stripe jitter characteristic factor threshold is multiplied by the stripe jitter characteristic factor threshold correction coefficient to obtain the stripe jitter characteristic factor correction threshold.

[0100] It should be noted that the above stripe jitter characteristic factor threshold correction coefficient is only a slight correction of the stripe jitter characteristic factor threshold.

[0101] It should be noted that the stripe jitter characteristic factor threshold is corrected by the stripe jitter characteristic factor threshold correction coefficient, and the stripe jitter adjustment effect is judged by the obtained stripe jitter characteristic factor correction threshold. Compared with using the stripe jitter characteristic factor threshold for judgment, a more stringent standard is used to judge the adjustment effect, which can accurately measure the effectiveness of the stripe jitter adjustment operation.

[0102] If the first stripe jitter characteristic factor is greater than the stripe jitter characteristic factor correction threshold, the stripe jitter adjustment effect information is invalid adjustment, and the image stripe jitter adjustment effect execution scheme is recorded as performing coupling interference judgment.

[0103] It should be noted that the first stripe jitter characteristic factor is greater than the stripe jitter characteristic factor correction threshold, which means that after the stripe jitter adjustment, the camera stripe jitter problem has not been solved, and the image stripe jitter is serious. Therefore, the stripe jitter adjustment effect information is invalid adjustment, and in order to ensure the imaging quality of the camera and eliminate identification interference, the image stripe jitter adjustment effect execution scheme is recorded as performing coupling interference judgment.

[0104] If the first stripe jitter representation factor is less than or equal to the stripe jitter representation factor correction threshold, the stripe jitter adjustment effect information is effective adjustment, and the image stripe jitter adjustment effect execution scheme is recorded as continuing the visitor scanning verification.

[0105] It should be noted that when the first stripe jitter representation factor is less than or equal to the stripe jitter representation factor correction threshold, it means that the image stripe jitter problem is solved after adjustment, the stripe jitter adjustment effect information is effective adjustment, the picture quality collected by the camera is improved, and the accuracy of scanning identification is enhanced. In order to reduce the power consumption of the system and reduce the complexity of the algorithm, the image stripe jitter adjustment effect execution scheme is recorded as continuing the visitor scanning verification.

[0106] When the image stripe jitter adjustment effect execution scheme is to perform coupling interference judgment, the coupling interference representation factor is analyzed, the coupling interference execution scheme is analyzed, the coupling interference is adjusted, and the coupling interference is eliminated to improve the image quality.

[0107] In this embodiment, the coupling interference representation factor is analyzed, and the specific analysis process is as follows:

[0108] The coupling interference representation parameters are collected, including the composite modulation depth of the current frame image, the coupling frequency domain feature ratio, and the space-time coupling interference index.

[0109] It should be noted that the composite modulation depth refers to the superimposed distortion degree of the stripe contrast and the rain and mist scattering effect, that is, the stripe modulation depth and the rain and mist scattering coefficient are multiplied by the respective weight coefficients and then added to obtain the composite modulation depth. The rain and mist scattering coefficient refers to the dark channel mean value. The coupling frequency domain feature ratio is the quantization of the coupling energy proportion of the frequency domain of the strobe and the raindrop dynamic noise, that is, the base frequency and its harmonic energy, the raindrop motion band energy, and the cross modulation energy are added, and then divided by the full frequency band energy. In rainy days, the coupling frequency domain feature ratio parameter is collected because raindrops will cause water droplet adhesion problems. The raindrops form irregular lenses on the lens surface, causing light to scatter in multiple directions and affecting the camera imaging quality. The space-time coupling interference index refers to the evaluation of the overlap degree of the stripe edge and the raindrop motion area in space-time, that is, the ratio of the overlap area of the edge area of the stripe and the raindrop motion area to the edge area of the stripe. In rainy days, the space-time coupling interference index parameter is collected because raindrops will cause water droplet lens distortion. The water droplets on the lens form aspherical lenses, affecting the camera imaging quality.

[0110] It should be noted that in weather conditions other than rainy days, the coupling frequency domain feature ratio and the space-time coupling interference index are set to zero, where the weather conditions other than rainy days include but are not limited to sunny days, cloudy days, and other weather conditions.

[0111] When the coupling frequency domain feature ratio increases, the cross modulation energy increases, and the spatio-temporal coupling interference index increases. The increase of the coupling frequency domain feature ratio and the spatio-temporal coupling interference index indicates strong coupling, and the composite modulation depth also increases accordingly.

[0112] It should be noted that the composite modulation depth can be obtained by Python+OpenCV, the coupling frequency domain feature ratio can be obtained by OpenCV+NumPy, and the spatio-temporal coupling interference index can be obtained by Python.

[0113] The coupling interference representation parameters are analyzed based on the coupling interference representation factors.

[0114] The preset reference composite modulation depth, reference coupling frequency domain feature ratio, and reference spatio-temporal coupling interference index in the database are extracted.

[0115] The composite modulation depth is compared with the reference composite modulation depth to obtain a composite modulation depth proportionality coefficient, i.e., the composite modulation depth divided by the reference composite modulation depth is taken as the composite modulation depth proportionality coefficient. The coupling frequency domain feature ratio is compared with the reference coupling frequency domain feature ratio to obtain a coupling frequency domain feature ratio proportionality coefficient, i.e., the coupling frequency domain feature ratio divided by the reference coupling frequency domain feature ratio is taken as the coupling frequency domain feature ratio proportionality coefficient. The spatio-temporal coupling interference index is compared with the reference spatio-temporal coupling interference index to obtain a spatio-temporal coupling interference index proportionality coefficient, i.e., the spatio-temporal coupling interference index divided by the reference spatio-temporal coupling interference index is taken as the spatio-temporal coupling interference index proportionality coefficient.

[0116] The composite modulation depth proportionality coefficient, coupling frequency domain feature ratio proportionality coefficient, and spatio-temporal coupling interference index proportionality coefficient are combined with corresponding metric coefficients for weighted coupling processing, thereby obtaining the coupling interference representation factors.

[0117] The coupling interference representation factors are the quantitative representation of the coupling interference of the composite modulation depth, coupling frequency domain feature ratio, and spatio-temporal coupling interference index of the current frame image.

[0118] It should be noted that the composite modulation depth metric coefficient, coupling frequency domain feature ratio metric coefficient, and spatio-temporal coupling interference index metric coefficient in the database are extracted.

[0119] In this embodiment, in order to realize the weighted coupling of the coupling interference characterization factor, a plurality of groups of parameter mapping relationships are constructed in advance in the database, which are used to associate different composite modulation depths, coupling frequency domain feature ratios, and spatio-temporal coupling interference indexes and their corresponding measurement coefficients. The mapping relationships are stored in the form of a structured configuration data table (for example, a measurement configuration table). The configuration table defines the measurement coefficient values corresponding to different composite modulation depths, coupling frequency domain feature ratios, and spatio-temporal coupling interference indexes. The above configuration table realizes the dynamic adaptation of the coupling interference characterization factor by combining the conditional matching and the coupling interference characterization parameter. The system can automatically extract the measurement coefficient matched with the coupling interference characterization parameter based on the configuration table, which is used for subsequent weighted coupling calculation. The value range of each measurement coefficient is limited to 0 to 1, and the sum of the three is 1, so as to ensure that the multi-feature fusion result has the normalization property and the physical consistency, and meets the weight constraint condition required by the coupling interference analysis.

[0120] In the specific implementation process, the coupling interference characterization factor is specifically represented as follows:

[0121] ,

[0122] wherein M is the coupling interference characterization factor, a is the composite modulation depth proportion coefficient, b is the coupling frequency domain feature ratio proportion coefficient, c is the spatio-temporal coupling interference index proportion coefficient, E1 is the composite modulation depth measurement coefficient, E2 is the coupling frequency domain feature ratio measurement coefficient, and E3 is the spatio-temporal coupling interference index measurement coefficient.

[0123] In this embodiment, the analysis of the coupling interference execution scheme is specifically as follows:

[0124] The coupling interference characterization factor threshold value is extracted.

[0125] The coupling interference characterization factor threshold value correction coefficient is extracted based on the first fringe jitter characterization factor.

[0126] It should be noted that the system has previously constructed the corresponding relationship between the first fringe jitter characterization factor and the corresponding coupling interference characterization factor threshold value correction coefficient in the database. The corresponding relationship is managed by a structured data table (such as a coupling interference correction coefficient table). In the matching process, the system uses the first fringe jitter characterization factor as the search key value to query the corresponding coupling interference characterization factor threshold value correction coefficient. The correction coefficient is used to dynamically adjust the coupling interference, so as to optimize the application of the coupling interference threshold value, improve the picture quality of the camera acquisition, and improve the recognition accuracy.

[0127] It needs to be explained that the greater the first stripe jitter representation factor is, the more serious the coupling interference problem is, and the poorer the picture quality collected by the camera is. In order to protect the imaging quality of the camera and eliminate the identification interference, the coupling interference representation factor threshold is strictly modified, so the corresponding coupling interference representation factor threshold modification coefficient is greater.

[0128] The coupling interference representation factor modification threshold is obtained based on the coupling interference representation factor threshold modification coefficient and the coupling interference representation factor threshold.

[0129] It needs to be explained that the coupling interference representation factor threshold modification coefficient and the coupling interference representation factor threshold are multiplied to obtain the coupling interference representation factor modification threshold.

[0130] It needs to be noted that the above coupling interference representation factor threshold modification coefficient only slightly modifies the coupling interference representation factor threshold.

[0131] It needs to be explained that the coupling interference representation factor threshold is modified by the coupling interference representation factor threshold modification coefficient, and the coupling interference representation factor is judged by the obtained coupling interference representation factor modification threshold. Compared with the judgment by the coupling interference representation factor threshold, the judgment standard is modified more strictly, the coupling interference execution scheme can be more accurately judged, the execution scheme is more targeted, the image generation problem can be solved, the coupling interference can be eliminated, and the image quality is improved.

[0132] If the coupling interference representation factor is greater than or equal to the coupling interference representation factor modification threshold, the coupling interference execution scheme is recorded as coupling interference adjustment.

[0133] It needs to be explained that the judgment of the coupling interference problem can improve the accuracy of the treatment, improve the system reliability, reduce the misjudgment rate of the identification, and make the identification more accurate. At this time, the coupling interference representation factor is greater than or equal to the coupling interference representation factor modification threshold, which indicates that the coupling interference is serious. In order to eliminate the coupling interference and improve the image quality, the coupling interference execution scheme is recorded as coupling interference adjustment.

[0134] If the coupling interference representation factor is less than the coupling interference representation factor modification threshold, the coupling interference execution scheme is recorded as sending warning information.

[0135] It needs to be explained that the coupling interference representation factor is less than the coupling interference representation factor modification threshold, which indicates that the coupling problem is not serious at this time. However, at this time, the judgment of the coupling interference is performed after the stripe jitter adjustment is invalid. In order to protect the system operation and not continue to produce error scanning results, the coupling interference execution scheme is recorded as sending warning information.

[0136] In this embodiment, the coupling interference adjustment is performed, and the specific process is as follows:

[0137] The coupling interference characteristic factor is subtracted from the coupling interference characteristic factor correction threshold to obtain a coupling interference characteristic factor deviation value.

[0138] It should be noted that the coupling interference characteristic factor is subtracted from the coupling interference characteristic factor correction threshold to obtain a coupling interference characteristic factor deviation value.

[0139] The frequency adjustment value and the light supplement intensity adjustment value are obtained based on the coupling interference characteristic factor deviation value.

[0140] It should be noted that the frequency adjustment value and the light supplement intensity adjustment value are both numerical data, and have no positive or negative meaning.

[0141] In this embodiment, the system pre-constructs a mapping relationship between the coupling interference characteristic factor deviation value and the corresponding frequency adjustment value and light supplement intensity adjustment value in the database, and the mapping relationship is managed in the form of a structured configuration table (for example, a coupling interference parameter configuration table). In the matching process, the system retrieves the corresponding frequency adjustment value and light supplement intensity adjustment value in the configuration table according to the coupling interference characteristic factor deviation value, taking the deviation value as a query key. By finding the frequency adjustment value and the light supplement intensity adjustment value that match the coupling interference characteristic factor deviation value, the system can dynamically adjust the coupling interference, thereby ensuring the quality of the obtained image.

[0142] It should be noted that the greater the coupling interference characteristic factor deviation value, the more serious the coupling interference problem at this time, which will cause the algorithm index to deteriorate, increase the false detection rate of the camera verification, cause the device recognition to fail, cover the key features of the scanning, and in order to obtain complete image information and improve the recognition accuracy, the extracted frequency adjustment value and light supplement intensity adjustment value are greater. Adjusting the coupling interference can improve the picture quality of the camera acquisition and improve the recognition accuracy.

[0143] Adjusting the frequency and the light supplement intensity can effectively solve the coupling interference problem and improve the quality of the obtained image. The core lies in precisely controlling the signal timing to eliminate the periodic characteristics of the interference signal and precisely destroying the physical interference mechanism. The frequency adjustment can break the resonance interference, and the use of high-frequency suppression can suppress visible jitter. The adjustment of the light supplement intensity can suppress the amplitude domain influence of the scattering noise. Therefore, adjusting the frequency and the light supplement intensity can solve the coupling interference problem and ensure the quality of the obtained image.

[0144] The light supplement frequency is additionally adjusted based on the rolling shutter line period of the camera and the frequency adjustment value.

[0145] It should be noted that the supplementary adjustment of the dimming frequency refers to multiplying the frequency adjustment value by the camera's rolling shutter period to obtain an integer multiple of the camera's rolling shutter period. In a specific embodiment, assuming the camera's rolling shutter period is 10μs and the frequency adjustment value is 1, then the execution value of the supplementary dimming frequency adjustment is 100kHz.

[0146] Obtain the infrared illumination intensity at this time.

[0147] It should be noted that the infrared illumination intensity at this time refers to the infrared radiation power received per unit area.

[0148] The intensity of the infrared supplementary light at this time can be obtained through an integrated optical sensor.

[0149] The supplementary light intensity is adjusted based on the supplementary light intensity adjustment value and the infrared supplementary light intensity at this time.

[0150] It should be noted that the value obtained by adding the supplementary light intensity adjustment value to the infrared supplementary light intensity at this time is used as the execution value for the supplementary adjustment of the supplementary light intensity.

[0151] When the image is subjected to external interference, the solution is to continue visitor scanning and verification; when the image is subjected to stripe jitter, the solution is to continue visitor scanning and verification; or when the image is subjected to stripe jitter adjustment, the solution is to continue visitor scanning and verification, the visitor identity is automatically verified based on the YOLOv5s target detection algorithm.

[0152] It should be noted that the automatic visitor identity verification is performed based on the YOLOv5s object detection algorithm. First, target face detection and liveness detection are performed, then facial features are extracted, and the facial features are compared with the database to obtain the final verification result. If the verification result is successful, the door is opened; if the verification result is unsuccessful, a prompt or alarm is issued.

[0153] like Figure 2 The diagram shown illustrates the structure of an automatic visitor identity verification system based on smart access control. This system includes:

[0154] The external interference judgment module uses the access control monitoring camera to sense whether a visitor is present within its scanning range in real time. When a visitor is detected, the module acquires the image information scanned by the camera at that time, obtains the average value of the dark channel of the image, analyzes the external interference of the image, and implements a solution to enhance the all-weather reliability of recognition in order to cope with complex scenarios.

[0155] The stripe jitter detection module analyzes stripe jitter characteristics and image stripe jitter detection schemes when performing image information analysis in the event of external interference, thereby improving the image quality captured by the camera and increasing recognition accuracy.

[0156] The stripe jitter adjustment module synchronously analyzes the deviation value of the stripe jitter characteristic factor when the image stripe jitter execution scheme is to perform stripe jitter adjustment, performs stripe jitter adjustment, analyzes the stripe jitter characteristic factor at this time after performing stripe jitter adjustment, analyzes the stripe jitter adjustment effect information, and thus obtains the image stripe jitter adjustment effect execution scheme, so as to guarantee the camera imaging quality and eliminate the recognition interference.

[0157] The coupling interference analysis module analyzes the coupling interference characteristic factor when the image stripe jitter adjustment effect execution scheme is to perform coupling interference judgment, analyzes the coupling interference execution scheme, performs coupling interference adjustment, eliminates the coupling interference, and improves the image quality.

[0158] The visitor identity verification module performs automatic visitor identity verification based on the YOLOv5s target detection algorithm when the image external interference execution scheme is to continue visitor scanning verification or the image stripe jitter execution scheme is to continue visitor scanning verification or the image stripe jitter adjustment effect execution scheme is to continue visitor scanning verification.

[0159] The above embodiments can be fully or partially implemented by software, hardware (such as a circuit), firmware, or any combination thereof. When implemented by software, the above embodiments can be fully or partially implemented in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the entire or partial processes or functions according to the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center through a wired (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0160] It should be understood that the term "and / or" as used herein merely describes an associated relationship, which means that there can be three relationships, for example, A and / or B, which can mean that A exists alone, A and B exist together, B exists alone, and A, B can be singular or plural. In addition, the character " / " herein generally represents an "or" relationship between the front and rear associated objects, but can also represent an "and / or" relationship, which can be understood in combination with the context before and after.

[0161] In the present application, "at least one" means one or more, and "multiple" means two or more. "At least one of the following" or the like means any combination of the items, including any combination of single or multiple items. For example, at least one of a, b, or c can mean a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.

[0162] It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of the processes should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0163] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0164] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working process of the above-described device, apparatus and unit can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0165] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit.

[0166] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0167] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for automatic visitor identity verification based on smart access control, characterized in that, Includes the following steps: The access control monitoring camera senses the presence of visitors within its scanning range in real time. When a visitor is detected, the camera acquires the image information it scans at that moment, obtains the dark channel mean value of the image, analyzes external interference in the image, and implements a solution to enhance the all-weather reliability of the recognition to cope with complex scenarios. The camera also obtains the dark channel mean value and extracts a threshold value. If the dark channel mean value is greater than the threshold value, the solution for external interference is marked as "image information analysis is performed"; if the dark channel mean value is less than or equal to the threshold value, the solution for external interference is marked as "continue visitor scanning and verification". When performing image information analysis, the external interference execution scheme analyzes the stripe jitter characterization factor and the image stripe jitter execution scheme to improve the image quality captured by the camera and increase the recognition accuracy. When the image stripe jitter removal scheme is to adjust the stripe jitter, the deviation value of the stripe jitter characterization factor is analyzed simultaneously, and stripe jitter adjustment is performed. After the stripe jitter adjustment is performed, the stripe jitter characterization factor is analyzed at this time, and the stripe jitter adjustment effect information is analyzed. Thus, the image stripe jitter adjustment effect implementation scheme is obtained to ensure the imaging quality of the camera and eliminate recognition interference. When the image stripe jitter adjustment effect implementation scheme is to judge coupling interference, the coupling interference characterization factor is analyzed, the coupling interference implementation scheme is analyzed, the coupling interference is adjusted, the coupling interference is eliminated, and the image quality is improved. When the image is subjected to external interference, the solution is to continue visitor scanning and verification; when the image is subjected to stripe jitter, the solution is to continue visitor scanning and verification; or when the image is subjected to stripe jitter adjustment, the solution is to continue visitor scanning and verification, the visitor identity is automatically verified based on the YOLOv5s target detection algorithm.

2. The method for automatic visitor identity verification based on smart access control according to claim 1, characterized in that, The analysis process for the stripe jitter characterization factor is as follows: Collect fringe jitter characterization parameters, including fringe modulation depth, frequency domain main peak energy ratio, and fringe coverage of the current frame image; Analysis of stripe jitter characterization factors based on stripe jitter characterization parameters; The fringe modulation depth, frequency domain main peak energy ratio, and fringe coverage are compared with their corresponding reference values. The comparison results are then weighted and coupled with the corresponding metric coefficients to obtain the fringe jitter characterization factor. The stripe jitter characterization factor is a quantitative representation of the stripe jitter state of the current frame image, which is jointly expressed by the stripe modulation depth, the frequency domain main peak energy ratio, and the stripe coverage.

3. The method for automatic visitor identity verification based on smart access control according to claim 1, characterized in that, The analysis of image stripe jitter execution scheme and the judgment process are as follows: Extract the threshold of the stripe jitter characterization factor; If the stripe jitter characterization factor is greater than the stripe jitter characterization factor threshold, then the image stripe jitter execution scheme is recorded as stripe jitter adjustment. If the stripe jitter characterization factor is less than or equal to the stripe jitter characterization factor threshold, then the image stripe jitter execution scheme is recorded as continuing visitor scanning verification.

4. The method for automatic visitor identity verification based on smart access control according to claim 3, characterized in that, The specific process for adjusting the stripe jitter is as follows: The difference between the stripe jitter characterization factor and the stripe jitter characterization factor threshold is used to obtain the stripe jitter characterization factor deviation value. Frequency adjustment value and duty cycle adjustment value are extracted based on the deviation value of the stripe jitter characterization factor. Obtain the rolling shutter cycle of the camera; The dimming frequency is supplemented and adjusted based on the camera's rolling shutter cycle and frequency adjustment value. Obtain the duty cycle of the dimming light source at this time; The duty cycle is adjusted by supplementing the duty cycle based on the duty cycle adjustment value and the duty cycle of the dimming light source at this time.

5. The method for automatic visitor identity verification based on smart access control according to claim 1, characterized in that, The analysis of the stripe jitter adjustment effect information yields the image stripe jitter adjustment effect implementation scheme. The analysis process is as follows: Obtain the fringe jitter characterization factor at this time, and denote it as the first fringe jitter characterization factor; Extract the threshold correction coefficient for stripe jitter characterization factors based on frequency adjustment values; The stripe jitter characterization factor correction threshold is obtained based on the stripe jitter characterization factor threshold and the stripe jitter characterization factor threshold correction coefficient; If the first fringe jitter characterization factor is greater than the fringe jitter characterization factor correction threshold, then the fringe jitter adjustment effect information is invalid adjustment, and the image fringe jitter adjustment effect execution scheme is recorded as performing coupling interference judgment. If the first stripe jitter characterization factor is less than or equal to the stripe jitter characterization factor correction threshold, then the stripe jitter adjustment effect information is considered effective, and the image stripe jitter adjustment effect execution plan is recorded as continuing visitor scanning verification.

6. The method for automatic visitor identity verification based on smart access control according to claim 1, characterized in that, The analysis of coupling interference characterization factors is as follows: Collect coupling interference characterization parameters, including the composite modulation depth, coupling frequency domain feature ratio, and spatiotemporal coupling interference index of the current frame image; Analysis of coupling interference characterization factors based on coupling interference characterization parameters; Extract the preset reference composite modulation depth, reference coupling frequency domain eigenvalue ratio, and reference spatiotemporal coupling interference index from the database; The composite modulation depth is compared with the reference composite modulation depth to obtain the composite modulation depth ratio coefficient; the coupled frequency domain feature ratio is compared with the reference coupled frequency domain feature ratio to obtain the coupled frequency domain feature ratio ratio coefficient; and the spatiotemporal coupling interference index is compared with the reference spatiotemporal coupling interference index to obtain the spatiotemporal coupling interference index ratio coefficient. The composite modulation depth scaling factor, the coupled frequency domain feature ratio scaling factor, and the spatiotemporal coupled interference index scaling factor are combined with the corresponding metric coefficients for weighted coupling processing to obtain the coupled interference characterization factor. The coupling interference characterization factor is a quantitative representation of coupling interference by the composite modulation depth, coupling frequency domain feature ratio, and spatiotemporal coupling interference index of the current frame image.

7. The method for automatic visitor identity verification based on smart access control according to claim 1, characterized in that, The analysis of the coupling interference execution scheme is as follows: Extract the threshold for coupling interference characterization factors; The threshold correction coefficient for the coupling interference characterization factor is extracted based on the first fringe jitter characterization factor. The correction threshold for the coupling interference characterization factor is obtained based on the correction coefficient for the coupling interference characterization factor threshold and the threshold for the coupling interference characterization factor. If the coupling interference characterization factor is greater than or equal to the coupling interference characterization factor correction threshold, then the coupling interference execution scheme is recorded as performing coupling interference judgment. If the coupling interference characterization factor is less than the coupling interference characterization factor correction threshold, then the coupling interference execution scheme is recorded as sending early warning information.

8. The method for automatic visitor identity verification based on smart access control according to claim 1, characterized in that, The specific process for adjusting coupling interference is as follows: The difference between the coupling interference characterization factor and the correction threshold of the coupling interference characterization factor is processed to obtain the deviation value of the coupling interference characterization factor. The frequency adjustment value and the supplementary light intensity adjustment value are obtained based on the deviation value of the coupling interference characterization factor. The dimming frequency is supplemented and adjusted based on the camera's rolling shutter cycle and frequency adjustment value. Obtain the infrared illumination intensity at this time; The supplementary light intensity is adjusted based on the supplementary light intensity adjustment value and the infrared supplementary light intensity at this time.

9. An automatic visitor identity verification system based on smart access control, employing the automatic visitor identity verification method based on smart access control as described in any one of claims 1-8, characterized in that, The system includes: The external interference judgment module uses the access control monitoring camera to sense whether a visitor is present within its scanning range in real time. When a visitor is detected, the module acquires the image information scanned by the camera at that time, obtains the average value of the dark channel of the image, analyzes the external interference of the image, and implements a solution to enhance the all-weather reliability of recognition in order to cope with complex scenarios. The stripe jitter judgment module analyzes stripe jitter characterization factors and image stripe jitter execution schemes when the image is subjected to external interference and the image information analysis is not performed, thereby improving the image quality captured by the camera and increasing recognition accuracy. The stripe jitter adjustment module, when the image stripe jitter execution scheme is to perform stripe jitter adjustment, simultaneously analyzes the deviation value of the stripe jitter characterization factor and performs stripe jitter adjustment. After the stripe jitter adjustment is performed, it analyzes the stripe jitter characterization factor at this time and analyzes the stripe jitter adjustment effect information, thereby obtaining the image stripe jitter adjustment effect execution scheme to ensure the camera imaging quality and eliminate recognition interference. The coupling interference analysis module analyzes coupling interference characterization factors and coupling interference execution schemes when the image stripe jitter adjustment effect is implemented, performs coupling interference adjustment, eliminates coupling interference, and improves image quality. The visitor identity verification module automatically verifies visitor identity based on the YOLOv5s target detection algorithm when the image is affected by external interference, image stripe jitter, or image stripe jitter adjustment.

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