Image deployment and control comparison method and device, computer equipment and storage medium

Abstract

The invention relates to an image deployment and control comparison method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a corrected image set and a deployment and control base library feature set, respectively updating deployment and control base library features in the corresponding deployment and control base library feature set based on corrected image features in the corrected image set to obtain at least one file feature set after the deployment and control base library feature set is updated, the features of the initial deployment and control base library are not updated; comparing the obtained features of the snapshot image to be detected with the element features in each archive feature set; when it is detected that the features of the snapshot image to be detected are successfully compared with the archive feature set, early warning information is generated; and when it is detected that the comparison between the to-be-detected snapshot image features and the archive feature set fails, the to-be-detected snapshot image features and the initial deployment and control base library features are compared, and if the comparison is successful, early warning information is generated. By adopting the method, the accuracy and efficiency of the monitoring alarm technology can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of control technology, and in particular to an image control comparison method, device, computer equipment and storage medium. Background Art

[0002] Monitoring refers to monitoring and tracking specific objects. Common monitoring methods include computer monitoring and manual monitoring. The existing manual monitoring methods usually consume high manpower and time costs, and the efficiency and accuracy of manual processing are usually related to the personal status of the technician, and its accuracy and efficiency are relatively unstable. The existing computer monitoring methods mostly compare the face images in the highly cohesive monitoring library with the captured images. If they match, it is considered that the monitoring personnel are captured and an alarm message is pushed. However, this solution has certain requirements for factors such as the front-end camera angle and external environment. When the image angle is not good and the quality is not high, it is easy to cause problems such as missed reports and false reports.

[0003] At present, no effective solution has been proposed for the problems of low accuracy and efficiency of the existing control and alarm technology. Summary of the invention

[0004] Based on this, it is necessary to provide an image control and comparison method, device, computer equipment and storage medium to address the above technical problems.

[0005] In a first aspect, the present application provides an image layout comparison method. The method comprises: Obtaining a corrected image set and a control base database feature set, and updating the control base database features in the corresponding control base database feature set based on the corrected image features in the corrected image set, respectively, to obtain at least one archive feature set after the control base database feature set is updated, and an initial control base database feature that has not been updated; Compare the acquired captured image features to be detected with the element features in each archive feature set; When it is detected that the features of the captured image to be detected are successfully compared with the archive feature set, a warning message is generated; when it is detected that the features of the captured image to be detected are failed to be compared with the archive feature set, the features of the captured image to be detected are compared with the features of the initial control base database. If the comparison is successful, a warning message is generated.

[0006] In one embodiment, a corrected image set and a control base database feature set are obtained, and based on the corrected image features in the corrected image set, the control base database features in the corresponding control base database feature set are updated respectively, including: Acquire a current initial corrected image set, wherein the initial corrected image set includes at least one initial feature data; Perform clustering calculation on the initial feature data in the current initial corrected image set to obtain the current image set, and update the control base database feature set based on the current image set; The next initial corrected image set is obtained, and based on a preset periodic duration, the above steps are repeated to periodically update the control base database features in the corresponding control base database feature set.

[0007] In one embodiment, the archive feature set includes at least: a control base database feature for the object to be detected, a main file feature for the object to be detected, sub-file data for the object to be detected, and at least one centroid feature; wherein the main file feature is the feature with the highest similarity to the corresponding control base database feature, the sub-file data is the photographed data for the object to be detected, and the centroid feature is the feature data with the highest similarity to the main file feature for the object to be detected in various time and space environments.

[0008] In one embodiment, the acquired captured image features to be detected are compared with the element features in each archive feature set; when it is detected that the captured image features to be detected are successfully compared with the archive feature set, an early warning message is generated, including: The features of the captured image to be detected are compared with the control base database features, main file features and centroid features in the archive feature set respectively; When it is detected that the number of successful comparisons between the captured image features to be detected and the features in the archive feature set is greater than or equal to a preset warning threshold, a warning message is generated.

[0009] In one embodiment, the acquired captured image features to be detected are compared with the element features in each archive feature set, including: Based on the control base database features and all centroid features, weighted processing is performed to obtain the first fitting feature for each archive feature set, and the features of the captured image to be detected are compared with the first fitting feature; If the comparison fails, weighted processing is performed based on the main file features and all centroid features to obtain a second fitting feature for each file feature set. The captured image features to be detected are compared with the second fitting feature. If the comparison is successful, a warning message is generated.

[0010] In one embodiment, weighted processing is performed based on the control base database features and all centroid features to obtain the first fitting feature for each archive feature set, including: Obtain a preset first weight matrix, and based on the first weight matrix, and the corresponding relationship between the control base database feature and the centroid feature, weightedly calculate the first fitting feature of each archive feature set, wherein the control base database feature corresponds to the first weight value in the first weight matrix, the centroid feature corresponds to the second weight value in the first weight matrix, and the first weight value is greater than the second weight value; Based on the main file features and all centroid features, weighted processing is performed to obtain the second fitting features for each file feature set, including: Obtain a preset second weight matrix, and based on the second weight matrix and the correspondence between the main file features and the centroid features, weightedly calculate the second fitting features of each file feature set, wherein the main file features correspond to the third weight value in the second weight matrix, the centroid features correspond to the fourth weight value in the second weight matrix, and the third weight value is greater than the fourth weight value.

[0011] In one embodiment, the acquired captured image features to be detected are compared with the element features in each archive feature set, including: Based on the characteristics of the controlled base database, the main file characteristics, and all centroid characteristics, weighted processing is performed to obtain the third fitting feature for each file feature set. The features of the captured image to be detected are compared with the third fitting feature. If the comparison similarity is detected to be greater than the preset similarity threshold, an early warning message is generated.

[0012] In one embodiment, calculating the fitting feature for each archival feature set includes: Obtain a preset third weight matrix, and based on the third weight matrix, and the corresponding relationship between the control base database feature, the main file feature and the centroid feature, weightedly calculate the third fitting feature of each archive feature set, wherein the control base database feature corresponds to the fifth weight value in the weight matrix, the main file feature corresponds to the sixth weight value in the weight matrix, the centroid feature corresponds to the seventh weight value in the weight matrix, the fifth weight value is greater than the sixth weight value, and the sixth weight value is greater than the seventh weight value.

[0013] In a second aspect, the present application also provides an image control comparison device. The device comprises: An acquisition module is used to acquire a corrected image set and a control base database feature set, and based on the corrected image features in the corrected image set, update the control base database features in the corresponding control base database feature set, respectively, to obtain at least one archive feature set after the control base database feature set is updated, and an initial control base database feature that has not been updated; A comparison module is used to compare the acquired captured image features to be detected with the element features in each archive feature set; The early warning module is used to generate early warning information when it is detected that the characteristics of the captured image to be detected are successfully compared with the archive feature set; when it is detected that the characteristics of the captured image to be detected are unsuccessful in comparison with the archive feature set, compare the characteristics of the captured image to be detected with the characteristics in the target feature set, excluding the archive feature set, and generate the early warning information if the comparison is successful.

[0014] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program: Obtaining a corrected image set and a control base database feature set, and updating the control base database features in the corresponding control base database feature set based on the corrected image features in the corrected image set, respectively, to obtain at least one archive feature set after the control base database feature set is updated, and an initial control base database feature that has not been updated; Compare the acquired captured image features to be detected with the element features in each archive feature set; When it is detected that the features of the captured image to be detected are successfully compared with the archive feature set, a warning message is generated; when it is detected that the features of the captured image to be detected are failed to be compared with the archive feature set, the features of the captured image to be detected are compared with the features of the initial control base database. If the comparison is successful, a warning message is generated.

[0015] The above-mentioned image control comparison method, device, computer equipment and storage medium, by correcting the corrected image features in the image set, respectively update the control base database features in the corresponding control base database feature set, and obtain multiple archive feature sets after the control base database feature set is updated, and the initial control base database features that have not been updated; the acquired captured image features to be detected are compared with the element features in each archive feature set; when it is detected that the comparison between the captured image features to be detected and the archive feature set is successful, an early warning message is generated, and when it is detected that the comparison between the captured image features to be detected and the archive feature set fails, the captured image features to be detected are compared with the initial control base database features, and if the comparison is successful, an early warning message is generated. The data quality of the control base database features can be improved through this application, and the captured image features to be detected are first compared with the archive features, and then compared with the initial control base database features, which can effectively improve the accuracy of the comparison results and reduce problems such as missed comparisons and wrong comparisons. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A diagram showing an application environment of an image control and comparison method in an embodiment; Figure 2 A schematic diagram of a flow chart of an image control comparison method in one embodiment; Figure 3A schematic diagram of the layout of the archive feature set structure in one embodiment; Figure 4 A schematic diagram of a flow chart of an image control comparison method in a preferred embodiment; Figure 5 It is a structural block diagram of an image control and comparison device in one embodiment; Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0018] The image control comparison method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Obtain a corrected image set and a control base database feature set, and based on the corrected image features in the corrected image set, update the control base database features in the corresponding control base database feature set, and obtain multiple archive feature sets after the control base database feature set is updated, and the initial control base database features that have not been updated; compare the acquired captured image features to be detected with the element features in each archive feature set, and when it is detected that the captured image features to be detected are successfully compared with the archive feature set, a warning message is generated, and when it is detected that the captured image features to be detected are failed to be compared with the archive feature set, the captured image features to be detected are compared with the initial control base database features, and if the comparison is successful, a warning message is generated. The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers.

[0019] In one embodiment, Figure 2 As shown, an image layout comparison method is provided, which is applied to Figure 1 The server in the example is used to illustrate the following steps: Step S202, obtain a corrected image set and a control base database feature set, and based on the corrected image features in the corrected image set, update the control base database features in the corresponding control base database feature set, to obtain at least one archive feature set after the control base database feature set is updated, and the initial control base database features that have not been updated.

[0020] Among them, the present application is mostly used in the scene of detecting or tracking one or more controlled objects, including but not limited to personnel, vehicles, etc. The above-mentioned control base library is generally a preset image base library for the controlled objects, such as the front photo and ID photo of the controlled personnel, or the license plate and appearance features of the controlled vehicles. In summary, the feature set of the above-mentioned control base library can be obtained by extracting features from the preset image base library of the controlled objects. The above-mentioned corrected image set is a set of images that can be used to update the control base database feature set. The method for obtaining the corrected image set includes taking a snapshot of the control object. It can be understood that the method for obtaining the corrected image set is not limited in this embodiment. All methods for obtaining the corrected image that can be used to update the control base database feature set should fall within the protection scope of this application. Preferably, in practical applications, the above-mentioned corrected image set can be image data of the control object captured by an image acquisition device such as a camera. The control base database is updated according to the corrected image set actually captured, which effectively avoids the influence of factors such as the more complex angles and the environment in which the control object is actually photographed, which reduces the accuracy of subsequent comparison results. Furthermore, the control base database feature set should be pre-acquired or pre-established based on the control object; and the corrected image set is obtained after the control base database feature set is established, and is used to update and correct the control base database feature set.

[0021] The corrected image set includes multiple corrected image features, and the corrected image features are based on the control object, that is, one control object corresponds to at least one corrected image feature, wherein the multiple different corrected image features corresponding to the same control object include but are not limited to images captured by the control object at different angles, images captured by the control object in different scenes, etc. Similarly, the control base database feature set includes multiple control base database features, each of which is a preset image feature corresponding to a control object.

[0022] After obtaining the corrected image set and the control base database feature set, the corresponding control base database features are updated based on the corrected image features to obtain an updated archive feature set, wherein the update method for the control base database features includes but is not limited to merging the corrected image features with the control base database features, that is, the updated archive feature set includes {initial control base database features, corrected image features}; or the corresponding non-updated initial control base database features can be replaced by higher-quality images in the corrected image features, etc. In practical applications, the obtained corrected image set is generally unable to update all control base database features. For example, the preset control base database feature set includes base database features of 100 control objects, but the obtained corrected image set only includes corrected image features of 10 of the control objects. Therefore, after updating the control base database features through the corrected image features, 10 archive feature sets corresponding to the above-mentioned corrected image features and 90 non-updated initial control base database features of the control objects are usually obtained.

[0023] Step S204 : Compare the acquired captured image features to be detected with the element features in each archive feature set.

[0024] Among them, each archive feature set includes multiple element features. Different element features in the same archive feature set reflect different features of the same controlled object, wherein the element features include but are not limited to image features of the controlled object captured in different scenes, image features of the controlled object at different angles, etc. After acquiring the captured image features to be detected, the captured image features to be detected are compared with the element features. It is understandable that the acquisition of the above-mentioned captured image features to be detected includes but is not limited to extracting the edges, corners, textures of the object to be detected in the image, or extracting image features based on deep learning methods, etc.

[0025] Step S206, when it is detected that the features of the captured image to be detected are successfully compared with the archive feature set, a warning message is generated; when it is detected that the features of the captured image to be detected are failed to be compared with the archive feature set, the features of the captured image to be detected are compared with the features of the initial control base database. If the comparison is successful, a warning message is generated.

[0026] Among them, firstly, the features of the captured image to be detected are compared with the archive feature set. If the comparison is successful, an early warning is issued, indicating that the control object is detected at this time. If the comparison fails, it is compared with the initial control base database features. Similarly, if the comparison is successful, an early warning is issued. Through steps S202 to S206, the control base database features are updated based on the corrected image set, and are compared with the archive feature set first during the comparison, which can realize the automatic expansion of the control image features of a single angle scene to multiple capture angles, multiple quality levels, and multiple time and space domains, reducing the quality requirements for the captured images to be detected, reducing the probability of missed ratios and wrong ratios, and improving the accuracy of control comparison.

[0027] In some embodiments, the method further comprises: Acquire a current initial corrected image set, wherein the initial corrected image set includes at least one initial feature data; Perform clustering calculation on the initial feature data in the current initial corrected image set to obtain the current image set, and update the control base database feature set based on the current image set; The next initial corrected image set is obtained, and based on a preset periodic duration, the above steps are repeated to periodically update the control base database features in the corresponding control base database feature set.

[0028] Specifically, an initial corrected image set is obtained, wherein the initial corrected image set is generally obtained by continuously capturing images in real time and their spatiotemporal domains (time, spatial location), external environment, etc., wherein the initial feature data reflects the features of different captured images. After obtaining the current initial corrected image set, the current initial corrected image set is clustered and archived to obtain the current image set, wherein the current initial corrected image set is clustered into clusters, and similar clusters are merged into a large archive set to obtain the above-mentioned current image set, and the clustering algorithm is used again based on the current image set and the control base database feature set to form an updated cluster set. Repeat the above method, and periodically use the merging algorithm to merge similar categories of the cluster set to form the above-mentioned archive feature set. In some preferred embodiments, each file feature set includes {main file feature, k centroid features, sub-file data set}, wherein the main file feature represents the best angle of the captured image target, wherein the determination of the main file feature can be determined according to the actual situation, such as the main file feature can be obtained based on the image with the most captured information of the controlled object; or the main file feature can be obtained based on the captured image closest to the controlled base image, and so on; the above k centroid features represent the feature data with the highest similarity to the main file feature in each spatiotemporal environment, wherein the spatiotemporal environment includes the spatiotemporal domain information, angle information, and the spatial and temporal domain information of the object to be detected. , scene information, etc., that is, the above-mentioned centroid feature indicates that in at least one spatiotemporal domain, the same target associated with the main file has high-quality or highest-quality images at different angles and in different scenes, where different angles include the front, side, back, and other angles of the controlled object, and different scenes include rainy days, foggy days, daytime, night, and other scenes; the above-mentioned sub-file data set records the captured information of the controlled object; the quality judgment can be determined by relevant technical personnel according to actual needs, and it is preferred that feature data with a high similarity to the main file feature (such as a similarity greater than 60%, or 80%, etc.) can be determined as a high-quality centroid feature. Through this embodiment, the control base database feature set can be periodically and automatically updated to enrich the archive feature set to improve the accuracy of control comparison.

[0029] In some embodiments, the archive feature set includes at least: a control base database feature for the object to be detected, a main file feature for the object to be detected, sub-file data for the object to be detected, and at least one centroid feature; wherein the main file feature is the feature with the highest similarity to the corresponding control base database feature, the sub-file data is the photographed data for the object to be detected, and the centroid feature is the feature data with the highest similarity to the main file feature for the object to be detected in various time and space environments.

[0030] Specifically, Figure 3The schematic diagram of the structure layout of the archive feature set in an embodiment, each archive unit in the set includes the control base library feature, the main file feature, multiple centroid features, and sub-file data. In this embodiment, the feature with the highest similarity to the control base library feature is used as the main file feature. Furthermore, in some preferred embodiments, the above-mentioned archive feature set also includes a spatiotemporal attribute set, which reflects the spatiotemporal information of the corresponding image when it is acquired. It can be understood that when comparing with the captured image feature to be detected, the spatiotemporal information of the two also needs to be compared.

[0031] In some embodiments, the method further comprises: The features of the captured image to be detected are compared with the control base database features, main file features and centroid features in the archive feature set respectively; When it is detected that the number of successful comparisons between the captured image features to be detected and the features in the archive feature set is greater than or equal to a preset warning threshold, a warning message is generated.

[0032] Specifically, each archive feature set includes at least one element feature, and the element feature includes but is not limited to the control base warehouse feature, main file feature and centroid feature described above. The features of the captured image to be detected are compared with the above element features one by one. If the number of successful comparisons is greater than the preset warning threshold, it means that the comparison between the captured image to be detected and the archive feature set is successful, and a warning message is generated at this time, wherein the above warning threshold is preferably 1, that is, an alarm is issued when one of the control base warehouse feature, main file feature, and multiple centroid features is compared with the captured image feature to be detected. This embodiment can improve the problems of missed ratios and wrong ratios caused by comparing the captured image with the base warehouse feature of a single angle when the captured image has a poor angle, insufficient clarity, or low quality, etc., reduce the quality requirements for the captured image, and improve the accuracy of the control comparison.

[0033] In some embodiments, the method further comprises: Based on the control base database features and all centroid features, weighted processing is performed to obtain the first fitting feature for each archive feature set, and the features of the captured image to be detected are compared with the first fitting feature; If the comparison fails, weighted processing is performed based on the main file features and all centroid features to obtain a second fitting feature for each file feature set. The captured image features to be detected are compared with the second fitting feature. If the comparison is successful, a warning message is generated.

[0034] Specifically, in this embodiment, the first fitting feature is calculated first, and the first fitting feature is obtained by weighted processing based on the control base database feature and all centroid features, and the first fitting feature is compared with the feature of the captured image to be detected. If the similarity between the two is higher than a preset threshold, an alarm is issued, wherein the threshold can be set to 60% or 80%, etc.; and if the similarity is not higher than the threshold, it is further compared with the second fitting feature, and the second fitting feature is obtained by weighted processing based on the main file feature and the centroid feature. If the similarity between the feature of the captured image to be detected and the second fitting feature is higher than the threshold, an early warning message is generated, otherwise no early warning is issued. In this embodiment, based on the centroid feature, the accuracy of the comparison hit for some captured images to be detected with incorrect angles and poor quality can be effectively improved, and the weighted calculation with the control base database feature or the main file feature can effectively reduce false alarms.

[0035] In some embodiments, weighted processing is performed based on the control base database features and all centroid features to obtain the first fitting feature for each archive feature set, including: Obtain a preset first weight matrix, and based on the first weight matrix, and the corresponding relationship between the control base database feature and the centroid feature, weightedly calculate the first fitting feature of each archive feature set, wherein the control base database feature corresponds to the first weight value in the first weight matrix, the centroid feature corresponds to the second weight value in the first weight matrix, and the first weight value is greater than the second weight value; Based on the main file features and all centroid features, weighted processing is performed to obtain the second fitting features for each file feature set, including: Obtain a preset second weight matrix, and based on the second weight matrix and the correspondence between the main file features and the centroid features, weightedly calculate the second fitting features of each file feature set, wherein the main file features correspond to the third weight value in the second weight matrix, the centroid features correspond to the fourth weight value in the second weight matrix, and the third weight value is greater than the fourth weight value.

[0036] Specifically, in this embodiment, the first fitting feature is first calculated according to the first weight matrix, wherein the first weight matrix can be defined as a (k+1)-row×1-column weight matrix W (k+1)x1 ={W s W0W1…W k-1} T , where W s is the weight corresponding to the control base database feature, that is, the first weight value mentioned above, W0 to W k-1 is the weight corresponding to the centroid feature, that is, the second weight value, which is the weight of each k centroid, and the first weight value is greater than the second weight value. In summary, the calculation of the first fitting feature f is: f=S i ×W s+centroid[0]×W0+ … centroid[k-1]×W k-1 Among them, Si is the above-mentioned control base database feature, centroid[0]…centroid[k-1] is the centroid feature of K different spatiotemporal environments. According to the above method, the first fitting feature of each archive feature set can be calculated.

[0037] Similarly, the features of the captured image to be detected are first compared with the first fitting features. If the comparison fails, they are compared with the second fitting features. In this embodiment, the second fitting features are calculated according to the second weight matrix. The second weight matrix can be defined as a (k+1) row × 1 column weight matrix W (k+1)x1 ={W m W0W1…W k-1} T , where W m is the main feature weight, that is, the third weight value, W0 to W k-1 is the weight corresponding to the centroid feature, i.e., the fourth weight value mentioned above, and the third weight value is greater than the fourth weight value. Further, the first weight value mentioned above may be equal to or different from the third weight value, which may be determined by relevant technical personnel. Similarly, the second weight value may be equal to or different from the fourth weight value. Preferably, the second weight value is equal to the fourth weight value.

[0038] In summary, the calculation of the second fitting feature f' is: f'=P i ×W m +centroid[0]×W0+ … centroid[k-1]×W k-1 Among them, P i is the above-mentioned main file feature. Similarly, centroid[0]…centroid[k-1] are the centroid features of K different spatiotemporal environments. According to the above method, the first fitting feature of each file feature set can be calculated.

[0039] Through this embodiment, by setting weights, the main file features and the control base database features are mainly used, and the centroid features are used as a supplement to calculate the fitting features for comparison with the object to be detected and captured. While ensuring the accuracy of the comparison, the probability of missed reports and false alarms is reduced.

[0040] In some embodiments, the method further comprises: Based on the characteristics of the controlled base database, the main file characteristics, and all centroid characteristics, weighted processing is performed to obtain the third fitting feature for each file feature set. The features of the captured image to be detected are compared with the third fitting feature. If the comparison similarity is detected to be greater than the preset similarity threshold, an early warning message is generated.

[0041] Specifically, in this embodiment, the control base library feature, the main file feature, and the centroid feature are uniformly weighted and calculated to obtain the third fitting feature, and the third fitting feature is compared with the captured image feature to be detected. If the comparison similarity is greater than the similarity threshold, an early warning message is generated, wherein the similarity threshold can be 60%. In this embodiment, the above three features are weighted and compared, and the standard is relatively strict, which effectively reduces the possibility of false alarms.

[0042] In some embodiments, the method further comprises: Obtain a preset third weight matrix, and based on the third weight matrix, and the corresponding relationship between the control base database feature, the main file feature and the centroid feature, weightedly calculate the third fitting feature of each archive feature set, wherein the control base database feature corresponds to the fifth weight value in the weight matrix, the main file feature corresponds to the sixth weight value in the weight matrix, the centroid feature corresponds to the seventh weight value in the weight matrix, the fifth weight value is greater than the sixth weight value, and the sixth weight value is greater than the seventh weight value.

[0043] Specifically, a third weight matrix can be preset first, and the elements in the weight matrix are all weight values ​​corresponding to different features, including weight matrices corresponding to the control base warehouse feature, the main file feature and the centroid feature. The third weight matrix can be set to a (k+2) row × 1 column weight matrix W (k+2)x1 = {w s , w m , w0, w1 … w k-1} T , where w s is the weight of the control base feature, i.e. the fifth weight value mentioned above, which has the highest weight; m is the main feature weight, i.e., the sixth weight value mentioned above, which is second only to the fifth weight value; w1…w k-1 is the seventh weight value, which is the lowest, and is the weight of k centroids. The calculation formula of the third fitting feature f'' is as follows: f''= s i × w s + p i × w m + centroid[0]× w0+ … + centroid[k-1] × w k-1 The features of the captured image to be detected are compared with the third fitting features. If the similarity is greater than a preset similarity threshold and the time and space environments are the same, it is considered to be a match and a corresponding warning information is generated.

[0044] The present application also provides a preferred embodiment of an image control comparison method. Figure 4 It is a flowchart of an image control and comparison method in a preferred embodiment.

[0045] Step S410, obtaining a control base database feature set. Taking the image material of the control object as the base database, extract the control object features based on the control image material and the material target structured attribute information to form a control image feature set S = {s i | 1≤i≤m}, thus obtaining the above-mentioned control base database feature set.

[0046] Step S420, obtaining an initial corrected image set. In this embodiment, the corrected image set is a plurality of scene snapshot images captured by various front-end camera devices, and the captured images are extracted using relevant algorithms to obtain features and attributes, and the initial corrected image set is obtained. The corrected image set includes the captured image features, and the target angle, time, weather, spatial point and other attribute information corresponding to the image features.

[0047] Step S430: cluster and archive the initial corrected image set. Cluster the captured initial corrected image set into clusters, merge similar clusters into large archive sets, and form a set of multiple target archives. For the captured image features and the historical captured image feature sets, use a clustering algorithm to form a cluster set P t , periodically use the merging algorithm to compare with the clustering set P t Similar categories are combined to form a modified image set P, P = {p j | 0<j≤n}, each file unit p j It contains {main file features, k centroid features, and sub-file data sets}, where the main file represents the best angle of the captured image target, and the k centroid features represent the centroid features of the best k images of different angles including front, side, back, etc., as well as different scenes such as day, night, rainy, foggy, etc. The merged archive set P is pushed to the above-mentioned control base library feature set, and the subsequent captured images are clustered and archived, and the previous process is repeated.

[0048] Step S440: Update the control base database feature set based on the modified image set. i | 1≤i≤m} as the control image feature set and, at the same time, periodically receive the clustered modified image set P={p j | 0<j≤n}, for each file p in P j With s in the bottom libraryi Compare them one by one, and if they match, the file p j To control the base members i If the file is not matched, skip it and select the next file p j+1 Continue to compare until all archive feature sets are traversed. After the update is completed, the updated SP set is obtained, where each SP i Elements include: {original control base database feature S i , Main file features p i , k centroid features, sub-file data, spatiotemporal domain attribute sets} and other field information.

[0049] Step S450, compare the captured image feature to be detected with the first fitting feature. The first fitting feature is the first fitting feature of each archive feature set obtained by weighted calculation based on the correspondence between the preset first weight matrix and the control base database feature and the centroid feature. If the comparison is successful, a warning message is generated. If not, jump to step S460.

[0050] Step S460, compare the captured image feature to be detected with the second fitting feature. The second fitting feature is a second fitting feature of each file feature set obtained by weighted calculation based on the correspondence between the preset second weight matrix and the main file feature and the centroid feature. If the comparison is successful, a warning message is generated. If the comparison is not successful, jump to step S470.

[0051] Step S470, compare the captured image features to be detected with the unupdated initial control base database features, and if the comparison is successful, generate an early warning message. The initial control base database features are those that have not been updated by the modified image set P. i .

[0052] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0053] Based on the same inventive concept, the embodiment of the present application also provides an image control comparison device for implementing the above-mentioned image control comparison method. The implementation solution provided by the device is similar to the implementation solution recorded in the above-mentioned method, so the specific limitations in one or more of the image control comparison device embodiments provided below can refer to the limitations of the image control comparison method above, and will not be repeated here.

[0054] In one embodiment, Figure 5 As shown, an image control and comparison device is provided, comprising: an acquisition module 51, a comparison module 52 and an early warning module 53, wherein: The acquisition module 51 is used to acquire the corrected image set and the control base database feature set, and based on the corrected image features in the corrected image set, respectively update the control base database features in the corresponding control base database feature set to obtain at least one archive feature set after the control base database feature set is updated, and the initial control base database feature that has not been updated; A comparison module 52 is used to compare the acquired captured image features to be detected with the element features in each archive feature set; The early warning module 53 is used to generate an early warning message when it is detected that the features of the captured image to be detected are successfully compared with the archive feature set; when it is detected that the features of the captured image to be detected are failed to be compared with the archive feature set, the features of the captured image to be detected are compared with the features in the target feature set, except the archive feature set. If the comparison is successful, an early warning message is generated.

[0055] Each module in the above-mentioned image layout control and comparison device can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0056] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 6 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store image control comparison related data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an image control comparison method is implemented.

[0057] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0058] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Obtaining a corrected image set and a control base database feature set, and updating the control base database features in the corresponding control base database feature set based on the corrected image features in the corrected image set, respectively, to obtain at least one archive feature set after the control base database feature set is updated, and an initial control base database feature that has not been updated; Compare the acquired captured image features to be detected with the element features in each archive feature set; When it is detected that the features of the captured image to be detected are successfully compared with the archive feature set, a warning message is generated; when it is detected that the features of the captured image to be detected are failed to be compared with the archive feature set, the features of the captured image to be detected are compared with the features of the initial control base database. If the comparison is successful, a warning message is generated.

[0059] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps: Obtaining a corrected image set and a control base database feature set, and updating the control base database features in the corresponding control base database feature set based on the corrected image features in the corrected image set, respectively, to obtain at least one archive feature set after the control base database feature set is updated, and an initial control base database feature that has not been updated; Compare the acquired captured image features to be detected with the element features in each archive feature set; When it is detected that the features of the captured image to be detected are successfully compared with the archive feature set, a warning message is generated; when it is detected that the features of the captured image to be detected are failed to be compared with the archive feature set, the features of the captured image to be detected are compared with the features of the initial control base database. If the comparison is successful, a warning message is generated.

[0060] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0061] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0062] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0063] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. An image control comparison method, characterized in that: The method comprises: Acquire a corrected image set and a control base database feature set, and based on the corrected image features in the corrected image set, respectively update the control base database features in the corresponding control base database feature set, to obtain at least one archive feature set after the control base database feature set is updated, and an initial control base database feature that has not been updated; Compare the acquired captured image features to be detected with the element features in each of the archive feature sets; When it is detected that the captured image feature to be detected is successfully compared with the archive feature set, a warning message is generated; when it is detected that the captured image feature to be detected is failed to be compared with the archive feature set, the captured image feature to be detected is compared with the initial control base database feature, and if the comparison is successful, the warning message is generated.

2. The method according to claim 1, characterized in that The obtaining of the corrected image set and the control base database feature set, and updating the corresponding control base database features in the control base database feature set based on the corrected image features in the corrected image set, respectively, includes: Acquire a current initial corrected image set, wherein the initial corrected image set includes at least one initial feature data; Performing cluster calculation on the initial feature data in the current initial corrected image set to obtain a current image set, and updating the control base database feature set based on the current image set; The next initial corrected image set is obtained, and based on a preset periodic duration, the above steps are repeated to periodically update the control base database features in the corresponding control base database feature set.

3. The method according to claim 1, characterized in that The archive feature set includes at least: a control base database feature for the object to be detected, a main file feature for the object to be detected, sub-file data for the object to be detected, and at least one centroid feature; wherein the main file feature is the feature with the highest similarity to the corresponding control base database feature, the sub-file data is the photographed data for the object to be detected, and the centroid feature is the feature data with the highest similarity to the main file feature for the object to be detected in various time and space environments.

4. The method according to claim 3, characterized in that The obtained captured image features to be detected are compared with the element features in each of the archive feature sets; when it is detected that the captured image features to be detected are successfully compared with the archive feature set, an early warning message is generated, including: Compare the captured image features to be detected with the control base database features, the main file features and the centroid features in the archive feature set respectively; When it is detected that the number of successful comparisons between the captured image features to be detected and the features in the archive feature set is greater than or equal to a preset warning threshold, the warning information is generated.

5. The method according to claim 3, characterized in that: The step of comparing the acquired captured image features to be detected with the element features in each of the archive feature sets includes: Based on the control base database feature and all the centroid features, weighted processing is performed to obtain a first fitting feature for each of the archive feature sets, and the captured image feature to be detected is compared with the first fitting feature; If a comparison failure is detected, weighted processing is performed based on the main file features and all the centroid features to obtain a second fitting feature for each of the file feature sets, and the captured image features to be detected are compared with the second fitting features. If a comparison success is detected, the warning information is generated.

6. The method according to claim 5, characterized in that The weighted processing based on the control base database feature and all the centroid features to obtain the first fitting feature for each of the archive feature sets includes: Obtain a preset first weight matrix, and based on the first weight matrix, and the corresponding relationship between the control base database feature and the centroid feature, weightedly calculate the first fitting feature of each of the archive feature sets, wherein the control base database feature corresponds to a first weight value in the first weight matrix, the centroid feature corresponds to a second weight value in the first weight matrix, and the first weight value is greater than the second weight value; The weighted processing based on the main file feature and all the centroid features to obtain the second fitting feature for each of the file feature sets includes: Obtain a preset second weight matrix, and based on the second weight matrix, and the corresponding relationship between the main file features and the centroid features, weightedly calculate the second fitting features of each of the file feature sets, wherein the main file features correspond to the third weight values ​​in the second weight matrix, the centroid features correspond to the fourth weight values ​​in the second weight matrix, and the third weight value is greater than the fourth weight value.

7. The method according to claim 3, characterized in that The step of comparing the acquired captured image features to be detected with the element features in each of the archive feature sets includes: Based on the characteristics of the controlled base database, the main file characteristics, and all the centroid characteristics, weighted processing is performed to obtain a third fitting feature for each of the archive feature sets, and the captured image feature to be detected is compared with the third fitting feature. If it is detected that the comparison similarity is greater than a preset similarity threshold, the warning information is generated.

8. The method according to claim 7, characterized in that The weighted processing based on the control base database feature, the main file feature, and all the centroid features to obtain the third fitting feature for each of the archive feature sets includes: Obtain a preset third weight matrix, and based on the third weight matrix, and the corresponding relationship between the control base database feature, the main file feature and the centroid feature, weightedly calculate the third fitting feature of each of the archive feature sets, wherein the control base database feature corresponds to the fifth weight value in the weight matrix, the main file feature corresponds to the sixth weight value in the weight matrix, the centroid feature corresponds to the seventh weight value in the weight matrix, the fifth weight value is greater than the sixth weight value, and the sixth weight value is greater than the seventh weight value.

9. An image control and comparison device, characterized in that: The device comprises: An acquisition module is used to acquire a corrected image set and a control base database feature set, and based on the corrected image features in the corrected image set, respectively update the control base database features in the corresponding control base database feature set to obtain at least one archive feature set after the control base database feature set is updated, and an initial control base database feature that has not been updated; A comparison module, used for comparing the acquired captured image features to be detected with the element features in each of the archive feature sets; The early warning module is used to generate early warning information when it is detected that the characteristics of the captured image to be detected are successfully compared with the archive feature set; when it is detected that the characteristics of the captured image to be detected are unsuccessful in comparison with the archive feature set, compare the characteristics of the captured image to be detected with the characteristics in the target feature set, excluding the archive feature set, and generate the early warning information if the comparison is successful.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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