User access detection method based on image acquisition

Abstract

The invention relates to the field of image detection, in particular to a user access detection method based on image acquisition, which comprises the following steps of: recording face information corresponding to a plurality of targets and associated user information, shooting real-time face information and environment information of a to-be-monitored target, and generating pre-learning data corresponding to the real-time face information; the face pre-learning data is learned to generate a face comparison result, the face comparison result is compared with a similarity threshold value, whether the face comparison result passes or not is judged, environment information is learned to generate an environment comparison result, whether the environment comparison result passes or not is judged according to the similarity threshold value, and whether access control is opened or not is judged according to a comprehensive comparison result. According to the method, the face of the to-be-monitored target is recognized by utilizing machine learning, so that the target is effectively prevented from being lost and mistakenly entered, automatic recognition and access control of the target and the associated user are realized, the safety of user access detection is improved, and more convenient and personalized services are provided for a target owner.

Description

Technical Field

[0001] The present invention relates to the field of image detection, and in particular to a method for detecting user entry and exit based on image acquisition. Background Art

[0002] With the advancement of technology, the automated monitoring and management of user access has become increasingly important. The user access detection method based on image acquisition mainly relies on computer vision technology, which uses cameras to capture the image or video data of the target, and uses image processing algorithms and machine learning models to analyze and identify the image, thereby realizing automatic monitoring of user access.

[0003] With the continuous development of deep learning technology, by combining multiple modal information such as images and sounds, more accurate and efficient machine learning models can be trained, which can achieve more comprehensive and accurate identification and management of users, and further improve the accuracy and efficiency of user entry and exit detection.

[0004] China Patent Authorization Announcement No.: CN109784208B discloses an image-based target behavior detection method, the steps are as follows: first, a plurality of image samples of each target posture are captured by a camera module, and the target posture in each image sample is marked and classified to obtain an image data set containing each target posture; a detection model is established, and the error rate and the number of iterative training are set; the image data set is imported into the detection model, and the detection model is trained according to the number of iterative training preset in S2 to obtain a detection model for detecting target behavior; when detecting the target behavior, the scene image of the target is captured by the camera module, and the captured scene image is input into the detection model, and the detection model extracts the information in the scene image to obtain the probability that the scene image belongs to a certain type of posture among various types of target postures. The invention provides an image-based target behavior detection method to help the owner understand the status of his target at any time.

[0005] Chinese patent application publication number: CN111507211A discloses a target supervision method, device, equipment and storage medium, which are used to detect whether the target active in the community is under the care of the owner and whether it is active within the specified time, so as to facilitate target supervision. The method includes: obtaining image data within a preset area; when it is determined that the acquisition time of the image data is within the first preset time period, performing target detection and portrait detection based on the image data; when it is determined that the target image is detected in the image data and the target image exists in a pre-established target database, if it is determined that the portrait detected in the image data does not include the target portrait, triggering the reporting of alarm information, wherein the target portrait is a portrait corresponding to the target image in the target database, and the target database stores the corresponding relationship between the target and the portrait of the target owner.

[0006] However, the above method has the following problems: the access control device cannot monitor the whereabouts of the target, thereby failing to reduce the risk of the target getting lost. Summary of the invention

[0007] To this end, the present invention provides a user entry and exit detection method based on image acquisition to overcome the problem that the access control equipment in the prior art cannot monitor the whereabouts of the target, thereby failing to reduce the risk of the target getting lost.

[0008] To achieve the above object, the present invention provides a user entry and exit detection method based on image acquisition, comprising:

[0009] Enter facial information and associated user information corresponding to several targets;

[0010] Capture the real-time facial information and environmental information of the target to be monitored;

[0011] Pre-learning the real-time facial information and generating corresponding pre-learning data;

[0012] The facial learning model learns the pre-learning data to generate a facial comparison result, compares the facial comparison result with a similarity threshold, and determines whether the facial comparison result is passed according to the comparison result;

[0013] The environmental information is learned to generate an environmental comparison result, and whether the environmental comparison result is passed is determined according to the similarity threshold;

[0014] In response to a passing facial comparison result or a passing environmental comparison result, a corresponding comprehensive comparison result is generated, and whether to open the door control is determined according to the comprehensive comparison result;

[0015] Wherein, when the real-time facial information is pre-learned, the real-time facial information is also filtered, and a number of facial pre-learning data with a learning rate of a standard learning rate are generated;

[0016] The standard learning rate is a learning rate that can be recognized by the facial learning model, which is inversely proportional to the resolution of the facial information. The higher the resolution, the lower the corresponding standard learning rate. For a single learning, the corresponding standard learning rate is a single learning rate.

[0017] The pre-learning data is standardized data that can be recognized by the facial learning model;

[0018] The facial learning model is generated through training based on a training set formed by a plurality of facial information.

[0019] Furthermore, the step of entering facial information and associated user information corresponding to a plurality of targets includes:

[0020] The terminal platform sends summary instructions to several sub-platforms;

[0021] The sub-platform captures corresponding facial information and collects corresponding associated user information;

[0022] The sub-platform sends the facial information and the associated user information to the terminal platform;

[0023] The associated user information includes user name, contact information and / or corresponding target associated information.

[0024] Furthermore, the step of photographing the real-time facial information and environmental information of the target to be monitored includes:

[0025] Several image acquisition devices are set up at the access control point;

[0026] When the monitor detects the target to be monitored, the transmitter sends a shooting instruction to the image acquisition device;

[0027] The image acquisition device photographs the target to be monitored to obtain corresponding real-time facial information and environmental information;

[0028] Transmitting the real-time facial information and the environmental information into a pre-learner;

[0029] Wherein, the image acquisition device is provided with a camera with adjustable angle.

[0030] Furthermore, the step of pre-learning the real-time facial information includes:

[0031] The pre-learner performs pixel filtering on the real-time facial information and forms corresponding filtering information;

[0032] Selecting several indicator features of the filtering information;

[0033] Standardizing the filtered information and generating corresponding pre-learning data;

[0034] Wherein, a pixel threshold is set in the pre-learner;

[0035] The indicator features include the pixel size, target breed and / or fur color of the filtered information;

[0036] The standardization process is to segment the filter information according to a standard learning rate.

[0037] Furthermore, the step of pixel filtering includes:

[0038] The pre-learner obtains pixel information of the real-time facial information;

[0039] comparing the pixel information with the pixel threshold;

[0040] When the pixel information is greater than the pixel threshold, the real-time facial information is retained, and when the pixel information is less than the pixel threshold, the real-time facial information is filtered.

[0041] Furthermore, the step of the face learning model learning the pre-learning data includes:

[0042] adjusting the learning rate of the face learning model to the standard learning rate;

[0043] Passing the pre-learning data into the facial learning model;

[0044] The facial learning model learns the pre-learning data and obtains corresponding facial comparison results.

[0045] Furthermore, the facial comparison result is compared with the similarity threshold, and when the facial comparison result is less than the similarity threshold, the transmitter sends a door access command, and the door access device closes the door access in response to the door access command.

[0046] Furthermore, when the facial comparison result is greater than the similarity threshold, the environmental information is learned to generate a corresponding environmental comparison result; when the environmental comparison result is less than the similarity threshold, the transmitter sends a door access command, and the door access device closes the door access in response to the door access command.

[0047] Further, when the environment comparison result is greater than the similarity threshold, the corresponding associated user information is matched according to the facial comparison result, and a confirmation message for going out is sent to the corresponding associated user. When the associated user confirms to go out, the door access control is opened in response to the going out instruction. When the associated user confirms to close the door, the door access control is closed in response to the closing instruction.

[0048] Wherein, the exit instruction is a instruction sent by the transmitter when the confirmation door opening information of the associated user is obtained;

[0049] The closing instruction is an instruction sent by the transmitter when the confirmation closing information of the associated user is obtained.

[0050] Furthermore, when the monitor detects more than one target to be monitored, the transmitter sends the access control instruction, and the access control device closes the access control in response to the access control instruction.

[0051] Compared with the prior art, the present invention records the facial information and associated user information corresponding to several targets, captures the real-time facial information and environmental information of the target to be monitored, and generates pre-learned data corresponding to the real-time facial information, learns the facial pre-learned data to generate a facial comparison result, compares it with the similarity threshold, and determines whether the facial comparison result passes, learns the environmental information to generate an environmental comparison result, and determines whether the environmental comparison result passes according to the similarity threshold, and determines whether to open the access control according to the comprehensive comparison result, and utilizes machine learning to perform facial recognition on the target to be monitored, which effectively avoids the loss and misentry of the target, realizes automatic recognition of the target and the associated user and access control, which not only improves the security of user entry and exit detection, but also provides more convenient and personalized services for the target owner.

[0052] Furthermore, by setting up a terminal platform and sending summary instructions to several sub-platforms, the sub-platforms send the collected facial information and associated user information to the terminal platform, which facilitates the collection of the target and corresponding associated user information, and facilitates the analysis and processing of the facial information and associated user information.

[0053] Furthermore, by setting a camera with an adjustable angle on the image acquisition device to shoot the real-time facial information of the monitored target, it can be ensured that the real-time facial image at the best angle is captured. Through this process, real-time facial information monitoring of the target can be achieved, which improves the accuracy of subsequent machine learning to identify the real-time facial image of the monitored target.

[0054] Furthermore, by performing pixel filtering on real-time facial information and selecting several indicator features to standardize the filtered information, it is convenient to remove noise and unimportant details, ensure the consistency and comparability of the data, and thus facilitate the extraction of clearer and more representative facial features.

[0055] Furthermore, by setting up a facial learning model, learning the pre-learning data and generating corresponding facial comparison results, it is possible to learn from the pre-learning data and improve its recognition ability, and ultimately achieve accurate recognition and comparison of real-time facial information of the monitored target.

[0056] Furthermore, by setting a similarity threshold and comparing the facial comparison result with the similarity threshold, it is ensured that only authorized monitored targets can pass through the access control, which increases security and convenience and provides a safe, convenient and automated target access management detection method.

[0057] By entering the target's facial information and associated user information, capturing real-time facial information, performing pre-learning and facial learning, and controlling access control based on facial comparison results, a complete user access detection method based on image acquisition is constructed. From information entry, real-time facial information acquisition, pre-learning, facial learning, to access control, a closed-loop management method is formed, which improves the security and convenience of target access management, reduces manual intervention, and improves the efficiency of user access detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flow chart of the user entry and exit detection method based on image acquisition of the present invention;

[0059] Figure 2 A flow chart of entering facial information and associated user information corresponding to a number of targets in the present invention;

[0060] Figure 3 This is a flow chart of photographing real-time facial information of a monitored target according to the present invention;

[0061] Figure 4 The present invention is a flow chart of pre-learning real-time facial information. DETAILED DESCRIPTION

[0062] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0063] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0064] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is only for description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0065] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0066] See also Figure 1 As shown, it is a flow chart of the user entry and exit detection method based on image acquisition of the present invention, including:

[0067] Step S1, input facial information corresponding to several targets and associated user information;

[0068] Step S2, photographing real-time facial information and environmental information of the target to be monitored;

[0069] Step S3, pre-learning the real-time facial information and generating corresponding pre-learning data;

[0070] Step S4, the facial learning model learns the pre-learning data to generate a facial comparison result, compares the facial comparison result with the similarity threshold, and determines whether the facial comparison result is passed according to the comparison result;

[0071] Step S5, learning the environmental information to generate an environmental comparison result, and judging whether the environmental comparison result is passed according to a similarity threshold;

[0072] Step S6, in response to the passing of the facial comparison result or the passing of the environmental comparison result, generating a corresponding comprehensive comparison result, and judging whether to open the door control according to the comprehensive comparison result;

[0073] During pre-learning, real-time facial information is filtered and a number of pre-learning data with a standard learning rate are generated;

[0074] The standard learning rate is the learning rate that the facial learning model can recognize. It is inversely proportional to the resolution of the facial information. The higher the resolution, the lower the corresponding standard learning rate. For single learning, the corresponding standard learning rate is a single learning rate.

[0075] The pre-learning data is standardized data that can be recognized by the face learning model;

[0076] The facial learning model is trained and generated based on a training set formed by several facial information.

[0077] In a specific implementation, when the learning rate is between 150 and 170 times per second, the facial learning model has the best learning effect on the pre-learning data and the best facial comparison result generation effect. When the resolution is 100 ppi, the preferred standard learning rate is 160 times per second.

[0078] By entering the facial information and associated user information corresponding to several targets, capturing the real-time facial information and environmental information of the target to be monitored, and generating pre-learning data corresponding to the real-time facial information, the facial pre-learning data is learned to generate a facial comparison result, which is compared with the similarity threshold to determine whether the facial comparison result passes, the environmental information is learned to generate an environmental comparison result, and the environmental comparison result is determined based on the similarity threshold to determine whether the environmental comparison result passes, and whether the access control is enabled based on the comprehensive comparison result. Machine learning is used to perform facial recognition on the target to be monitored, which effectively avoids the loss and misentry of the target, and realizes automatic recognition and access control of the target and the associated user, which not only improves the security of user entry and exit detection, but also provides more convenient and personalized services for the target owner.

[0079] See also Figure 2 As shown, it is a flow chart of the present invention for entering facial information corresponding to several targets and associated user information, including:

[0080] Step S11, the terminal platform sends a summary instruction to several sub-platforms;

[0081] Step S12, the sub-platform captures the corresponding facial information and collects the corresponding associated user information;

[0082] Step S13, the sub-platform sends the facial information and associated user information to the terminal platform;

[0083] The associated user information includes user name, contact information and / or corresponding target associated information.

[0084] In the specific implementation, after receiving the summary instruction, each sub-platform starts to capture the target's facial information and collects the associated user information related to these targets at the same time. When collecting the associated user information, the user's name, contact information and other information are obtained through user input or database query.

[0085] By setting up a terminal platform and sending summary instructions to several sub-platforms, the sub-platforms send the collected facial information and related user information to the terminal platform, which facilitates the collection of the target and the corresponding related user information, and facilitates the analysis and processing of the facial information and the related user information.

[0086] See also Figure 3 As shown, it is a flow chart of the present invention for photographing real-time facial information of a monitored target, including:

[0087] Step S21, setting up a number of image acquisition devices at the access control point, specifically, they can be set at the door handle position to facilitate shooting;

[0088] Step S22, when the monitor detects the target to be monitored, the transmitter sends a shooting instruction to the image acquisition device;

[0089] Step S23, the image acquisition device takes a picture of the monitored target to obtain corresponding real-time facial information and environmental information;

[0090] Step S24, transferring the real-time facial information and environmental information to the pre-learner;

[0091] Wherein, the image acquisition device is provided with a camera with adjustable angle.

[0092] In a specific implementation, the target to be monitored can be a human or an animal, and the monitor can be a motion sensor or other types of sensors. When the target is detected approaching the access control area, the transmitter is automatically triggered to send a shooting instruction to the image acquisition device, and by automatically responding to the appearance of the target, it is ensured that the target's facial information can be captured in time when it reaches the access control area.

[0093] The camera in the image acquisition device can adjust its angle to adapt to different shooting environments and target heights to ensure that the best facial image can be captured. During the shooting process, it should be ensured that the image quality meets the requirements of facial recognition, such as sufficient clarity and contrast.

[0094] In a specific implementation, the image acquisition device includes an error handling mechanism, and when shooting fails or the image quality is poor, the image is retaken or the maintenance personnel are notified to check the equipment.

[0095] By setting a camera with an adjustable angle on the image acquisition device to shoot the real-time facial information of the monitored target, it can be ensured that the real-time facial image at the best angle is captured. Through this process, real-time facial information monitoring of the target can be achieved, which improves the accuracy of subsequent machine learning to identify the real-time facial image of the monitored target.

[0096] See also Figure 4 As shown, it is a flow chart of the present invention for pre-learning real-time facial information, including:

[0097] Step St1, the pre-learner performs pixel filtering on the real-time facial information and forms corresponding filtering information;

[0098] Step St2, selecting several indicator features of the filtering information;

[0099] Step St3, standardize the filtered information and generate corresponding pre-learning data;

[0100] Wherein, a pixel threshold is set in the pre-learner;

[0101] Indicator features include pixel size of the filtered information, target breed and / or coat color;

[0102] The normalization process is to divide the filtered information according to the standard learning rate.

[0103] Image filtering techniques (such as mean filtering, Gaussian filtering, etc.) are applied to smooth the image and reduce noise. Edge detection algorithms (such as Canny edge detection) are used to highlight facial contours and feature edges. According to the needs of facial recognition, a series of key facial features are selected, such as the position and shape of the eyes, nose, and mouth, the contour and symmetry of the face, and skin texture. These features should have sufficient discrimination to represent different individuals.

[0104] The selected features are standardized to ensure their consistency and comparability between different images. Standardization includes scaling feature values ​​to a specific range (such as 0-1 or -1 to 1), or normalizing them (such as converting feature values ​​to relative values) and segmenting the filtered information at a standard learning rate. Through standardization, the scale and distribution differences between different images can be eliminated, improving the accuracy of subsequent feature extraction, thereby improving the accuracy and efficiency of tasks such as facial recognition.

[0105] By filtering the real-time facial information by pixels and selecting several indicator features to standardize the filtered information, it is convenient to remove noise and unimportant details, ensure the consistency and comparability of the data, and thus facilitate the extraction of clearer and more representative facial features.

[0106] Specifically, the steps of pixel filtering include:

[0107] The pre-learner obtains pixel information of real-time facial information;

[0108] comparing the pixel information to a pixel threshold;

[0109] When the pixel information is greater than the pixel threshold, the real-time facial information is retained, and when the pixel information is less than the pixel threshold, the real-time facial information is filtered.

[0110] In the specific implementation, a pixel threshold is set to decide whether to retain the facial information captured in real time. Specifically, when the pixel information (such as brightness, grayscale value or a specific color channel value) is greater than the set threshold, the pixel is considered to be valid facial information and is therefore retained; when the pixel information is less than the threshold, the pixel is considered to be background, noise or unimportant details and is therefore filtered out.

[0111] This method is very useful in the pre-learning stage of face recognition or facial feature extraction, because it can help remove irrelevant information in the image, thereby highlighting facial features and improving the accuracy and efficiency of subsequent processing. However, setting an appropriate pixel threshold is a key issue. If the threshold is set too high, some important facial information may be mistakenly filtered out; if the threshold is set too low, it may not effectively remove noise and background information.

[0112] In order to determine the appropriate pixel threshold, some experimentation and adjustment is usually required. This includes observing the effects of images filtered at different thresholds and evaluating the performance of these images in subsequent facial recognition or feature extraction tasks. Through repeated trials and optimization, an optimal threshold that can effectively remove noise while retaining key facial information can be found.

[0113] In addition, it is worth noting that the filtering method based on pixel threshold may not be suitable for all situations. For example, this method may fail in some scenes with poor lighting conditions or unclear facial features. Therefore, in practical applications, it is necessary to combine other pre-learning techniques (such as image enhancement, denoising, etc.) and more advanced feature extraction and recognition algorithms to improve the accuracy and robustness of facial recognition.

[0114] Preferably, the pixel threshold is usually set to 1080px.

[0115] Among some possible implementations:

[0116] The pixel threshold is set to 1080px. If the pixel information of A's real-time facial information is 1000px, which is less than the pixel threshold, the pre-learner will filter A's real-time facial information.

[0117] Among some possible implementations:

[0118] The pixel threshold is set to 1080px. If the pixel information of B's ​​real-time facial information is 2000px, which is greater than the pixel threshold, the pre-learner will retain B's real-time facial information.

[0119] Among some possible implementations:

[0120] The pixel threshold is set to 900px. If the pixel information of C's real-time facial information is 1000px, which is greater than the pixel threshold, the pre-learner will retain C's real-time facial information.

[0121] Specifically, the steps for the face learning model to learn the pre-learning data include:

[0122] Adjust the learning rate of the face learning model to the standard learning rate;

[0123] Pass the pre-learned data into the face learning model;

[0124] The face learning model learns the pre-learning data and obtains the corresponding face comparison results.

[0125] In practice, the learning rate is an important hyperparameter in the model training process, which determines the step size of the model weight update in each iteration. A suitable learning rate can help the model converge to the optimal solution faster, while an excessively large learning rate may cause the model to oscillate or even diverge near the optimal solution, and an excessively small learning rate will make the model training process very slow.

[0126] Standard learning rate: Before the training of the face learning model begins, the learning rate is usually set to a preset "standard" value. This value may be derived through previous experiments or experience, or it may be estimated based on the complexity of the model and the size of the data.

[0127] Adjusting the learning rate: In the actual training process, the learning rate may be dynamically adjusted according to the training performance of the model (such as the rate of decrease of the loss function). This can be achieved through techniques such as learning rate decay and learning rate scheduler.

[0128] Pre-learning data refers to facial image data that has been pre-learned. This data will be used to train the facial learning model so that it can learn facial features and be used for subsequent facial recognition or classification tasks.

[0129] The process of learning the pre-learning data by the facial learning model refers to the process of minimizing a certain loss function by iteratively optimizing its internal parameters (such as weights and biases). The loss function is usually used to measure the difference between the model's predicted results and the actual results.

[0130] Forward propagation: In each iteration, the facial learning model receives a batch of pre-learning data as input, and performs forward propagation through its internal network structure (such as convolutional layer, pooling layer, fully connected layer, etc.), and finally outputs facial comparison results or classification results.

[0131] Calculate loss: Compare the output of the face learning model with the true label and calculate the loss value.

[0132] Back propagation: According to the loss value, the gradient of each parameter is calculated by the chain rule, and the parameters of the facial learning model are updated in the opposite direction of the gradient to obtain the facial comparison result.

[0133] The facial comparison results refer to the results obtained by the model after feature extraction and comparison of the input facial images during or after training. These results can be used for tasks such as facial recognition and identity verification.

[0134] Feature extraction: The facial learning model extracts facial features of the input image through the learned facial feature representation.

[0135] Feature matching: The extracted facial features are compared with the features in the database to determine whether the input image matches an individual in the database.

[0136] By setting up a facial learning model, learning the pre-learning data and generating corresponding facial comparison results, it is possible to learn from the pre-learning data and improve its recognition ability, and ultimately achieve accurate recognition and comparison of the real-time facial information of the monitored target.

[0137] Specifically, the facial comparison result is compared with a similarity threshold, and when the facial comparison result is less than the similarity threshold, the transmitter sends a door access command, and the door access device closes the door access in response to the door access command.

[0138] In a specific implementation, when the facial comparison result does not meet the similarity requirement (ie, is less than the similarity threshold), the transmitter (which may be a controller or a computer of the access control system) will send an access control command.

[0139] This instruction is usually an electrical signal or network message used to instruct the access control device to perform a specific action.

[0140] Access control device response: After receiving the access control command, the access control device (such as electromagnetic lock, access control gate, etc.) will perform corresponding actions according to the content of the command.

[0141] In this scenario, the access control device will respond to the access control command to close the access control, that is, prevent unauthorized monitored targets from entering.

[0142] It should be noted that in practical applications, the security of the access control system is of vital importance. Therefore, it is necessary to ensure the accuracy and robustness of the facial learning model, the rationality of the similarity threshold, and pay attention to privacy protection. When processing facial data, it is necessary to comply with relevant privacy protection regulations and standards to ensure the legality and security of personal information. Secondly, user experience is also very important. The design and implementation of the access control system needs to consider user experience, such as recognition speed, false alarm rate, user feedback, etc.

[0143] The facial comparison result indicates the degree of similarity between the real-time facial information and the preset facial information, and the similarity threshold is a preset standard used to determine whether the real-time facial image and the preset facial information are similar enough. If the facial comparison result is less than the similarity threshold, it means that the facial images are too different to be considered as the same target. At this time, security measures need to be taken to close the access control.

[0144] Among some possible implementations:

[0145] The similarity threshold is set to 80%. If the facial comparison result is 70%, the facial comparison result is less than the similarity threshold, and the condition for closing the access control is met, the transmitter sends a access control command, and the access control device responds to the access control command to close the access control.

[0146] Specifically, when the facial comparison result is greater than the similarity threshold, the environmental information is learned to generate the corresponding environmental comparison result. When the environmental comparison result is less than the similarity threshold, the transmitter sends a door access command, and the door access device responds to the door access command to close the door access.

[0147] If the facial comparison result is greater than the similarity threshold, the system automatically considers the real-time facial information to be similar enough to the preset facial information and may be the same target. If the environmental comparison result is less than the similarity threshold, the system considers the environment to be unsafe and does not execute the access control command.

[0148] Among some possible implementations:

[0149] The similarity threshold is set to 80%. If the facial comparison result is 85%, the facial comparison result is greater than the similarity threshold, and the environmental information is learned to generate the corresponding environmental comparison result.

[0150] By setting a similarity threshold and comparing the facial comparison result with the similarity threshold, it ensures that only authorized monitored targets can pass through the access control, increasing security and convenience, and providing a safe, convenient and automated target access management detection method.

[0151] Specifically, when the environment comparison result is greater than the similarity threshold, the corresponding associated user information is matched according to the facial comparison result, and a confirmation message for going out is sent to the corresponding associated user. When the associated user confirms going out, the door access control is opened in response to the going out instruction. When the associated user confirms closing the door, the door access control is closed in response to the closing instruction.

[0152] Among them, the exit command is a command sent by the transmitter when the confirmation information of the associated user is obtained;

[0153] The closing instruction is a instruction sent by the transmitter when the confirmation closing information of the associated user is obtained.

[0154] In a specific implementation, when the environment comparison result is greater than the similarity threshold, the system will search for the associated user information that matches the facial feature vector and send a confirmation message for going out. The confirmation message for going out may contain some basic information, such as the user's name, going out time, access control location, etc. It may also contain some additional information, such as the authorization status of going out (whether allowed), the destination prompt after going out, etc. The confirmation message for going out can be sent to the associated user in a variety of ways, such as mobile phone text messages, emails, APP push notifications, etc. The choice of sending method depends on the user's preference and system configuration.

[0155] After receiving the confirmation message of going out, the user can take corresponding actions according to the content of the message, such as confirming going out, canceling going out, etc.

[0156] The system can also perform follow-up processing based on user feedback, such as updating access control status, recording exit logs, etc.

[0157] It should be noted that:

[0158] 1. Accuracy of access control: Ensure the accuracy and robustness of the facial learning model to reduce false positives and false negatives.

[0159] 2. Real-time access control: The access control system needs to be able to respond quickly to facial comparison results and send a confirmation message within the user's waiting time.

[0160] 3. Security of access control: protect users’ privacy information and ensure the security and confidentiality of messages during sending.

[0161] 4. User experience: Provide a friendly user interface and operation process to improve user satisfaction and convenience.

[0162] The associated user confirms closing the door in some way (such as a button on a mobile phone APP, touch sensing on the access control panel, voice command, etc.). Once the system receives the information that the associated user confirms closing the door, it triggers an internal mechanism to generate a closing instruction. This closing instruction is an electrical signal or network message specifically used to instruct the access control device to perform a closing action.

[0163] After receiving a closing command, the access control device (such as an electromagnetic lock, access control gate, sliding door, etc.) will execute the closing action according to the content of the command.

[0164] This may involve the movement of mechanical structures, the locking of electronic locks, or the establishment of other forms of physical barriers to prevent unauthorized access to the target being monitored.

[0165] After the access control is successfully closed, the system usually updates its internal status record and marks the access control as currently closed. This helps the system monitor and record the use of the access control and provides a basis for subsequent access control decisions.

[0166] In some cases, the system may send a message or prompt to the user to confirm that the door is closed to enhance user trust and satisfaction. This can be achieved through mobile phone APP notifications, SMS, email or other communication methods.

[0167] Specifically, when the monitor detects more than one target to be monitored, the transmitter sends a door access command, and the door access device closes the door access in response to the door access command.

[0168] In access control systems, sensors are configured to identify and count objects within a specific area, such as in front of a gated entrance.

[0169] The target to be monitored may be a target that is pre-registered and authorized in the system, or any target may be considered as an object to be monitored according to the settings.

[0170] The monitor uses a camera to monitor the number of objects in front of the access control entrance.

[0171] In a specific implementation, the camera identifies the target through facial recognition, body feature recognition, weight sensing or infrared sensing, and calculates the number of targets that appear in the monitoring area at the same time.

[0172] When the monitor detects more than one target to be monitored, an internal mechanism is triggered to determine whether a door access control command needs to be sent.

[0173] If the system is configured to allow only a single target to pass or to require special authorization to allow multiple targets to pass, and the current situation does not meet these conditions, the monitor will send a door access command to the door control system's transmitter.

[0174] By entering the target's facial information and associated user information, capturing real-time facial information, performing pre-learning and facial learning, and controlling access control based on facial comparison results, a complete user access detection method based on image acquisition is constructed. From information entry, real-time facial information acquisition, pre-learning, facial learning, to access control, a closed-loop management method is formed, which improves the security and convenience of target access management, reduces manual intervention, and improves the efficiency of user access detection.

[0175] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0176] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A user entry and exit detection method based on image acquisition, characterized in that: include: Enter facial information and associated user information corresponding to several targets; Capture the real-time facial information and environmental information of the target to be monitored; Pre-learning the real-time facial information and generating corresponding pre-learning data; The facial learning model learns the pre-learning data to generate a facial comparison result, compares the facial comparison result with a similarity threshold, and determines whether the facial comparison result is passed according to the comparison result; The environmental information is learned to generate an environmental comparison result, and whether the environmental comparison result is passed is determined according to the similarity threshold; In response to a passing facial comparison result or a passing environmental comparison result, a corresponding comprehensive comparison result is generated, and whether to open the door control is determined according to the comprehensive comparison result; Wherein, when the real-time facial information is pre-learned, the real-time facial information is also filtered, and a number of facial pre-learning data with a learning rate of a standard learning rate are generated; The standard learning rate is a learning rate that can be recognized by the facial learning model, which is inversely proportional to the resolution of the facial information. The higher the resolution, the lower the corresponding standard learning rate. For a single learning, the corresponding standard learning rate is a single learning rate. The pre-learning data is standardized data that can be recognized by the facial learning model; The facial learning model is generated through training based on a training set formed by a plurality of facial information.

2. The user entry and exit detection method based on image acquisition according to claim 1, characterized in that: The steps of entering facial information corresponding to a number of targets and associated user information include: The terminal platform sends summary instructions to several sub-platforms; The sub-platform captures corresponding facial information and collects corresponding associated user information; The sub-platform sends the facial information and the associated user information to the terminal platform; The associated user information includes user name, contact information and / or corresponding target associated information.

3. The user entry and exit detection method based on image acquisition according to claim 2, characterized in that: The step of photographing the real-time facial information and environmental information of the target to be monitored includes: Several image acquisition devices are set up at the access control point; When the monitor detects the target to be monitored, the transmitter sends a shooting instruction to the image acquisition device; The image acquisition device photographs the target to be monitored to obtain corresponding real-time facial information and environmental information; Transmitting the real-time facial information and the environmental information into a pre-learner; Wherein, a camera with adjustable angle is arranged in the image acquisition device.

4. The user entry and exit detection method based on image acquisition according to claim 3 is characterized in that: The step of pre-learning the real-time facial information includes: The pre-learner performs pixel filtering on the real-time facial information and forms corresponding filtering information; Selecting several indicator features of the filtering information; Standardizing the filtered information and generating corresponding pre-learning data; Wherein, a pixel threshold is set in the pre-learner; The indicator features include the pixel size, target breed and / or fur color of the filtered information; The standardization process is to segment the filter information according to a standard learning rate.

5. The method for detecting user entry and exit based on image acquisition according to claim 4, characterized in that: The steps of pixel filtering include: The pre-learner obtains pixel information of the real-time facial information; comparing the pixel information with the pixel threshold; When the pixel information is greater than the pixel threshold, the real-time facial information is retained, and when the pixel information is less than the pixel threshold, the real-time facial information is filtered.

6. The method for detecting user entry and exit based on image acquisition according to claim 5, characterized in that: The step of the face learning model learning the pre-learning data comprises: adjusting the learning rate of the face learning model to the standard learning rate; Passing the pre-learning data into the facial learning model; The facial learning model learns the pre-learning data and obtains corresponding facial comparison results.

7. The method for detecting user entry and exit based on image acquisition according to claim 6, characterized in that: The facial comparison result is compared with the similarity threshold, and when the facial comparison result is less than the similarity threshold, the transmitter sends a door control instruction, and the door control device closes the door control in response to the door control instruction.

8. The method for detecting user entry and exit based on image acquisition according to claim 7, characterized in that: When the facial comparison result is greater than the similarity threshold, the environmental information is learned to generate a corresponding environmental comparison result. When the environmental comparison result is less than the similarity threshold, the transmitter sends a door access command, and the door access device closes the door access in response to the door access command.

9. The method for detecting user entry and exit based on image acquisition according to claim 8, characterized in that: When the environment comparison result is greater than the similarity threshold, the corresponding associated user information is matched according to the facial comparison result, and a confirmation message for going out is sent to the corresponding associated user. When the associated user confirms to go out, the door access control is opened in response to the going out instruction. When the associated user confirms to close the door, the door access control is closed in response to the closing instruction. Wherein, the exit instruction is a instruction sent by the transmitter when the confirmation door opening information of the associated user is obtained; The closing instruction is an instruction sent by the transmitter when the confirmation closing information of the associated user is obtained.

10. The method for detecting user entry and exit based on image acquisition according to claim 9, characterized in that: When the monitor detects more than one target to be monitored, the transmitter sends the access control instruction, and the access control device closes the access control in response to the access control instruction.

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