Image acquisition method and related apparatus

Abstract

Embodiments of the present application provide an image acquisition method and related device, which are applied to an image acquisition system, the image acquisition system comprises K image acquisition devices, the same target image acquisition area is included in the image acquisition area of the K image acquisition devices, and the method comprises the following steps: acquiring people flow information and environment information of the target image acquisition area; determining M target image acquisition devices from the K image acquisition devices according to the people flow information and the environment information; and performing image acquisition on the target image acquisition area through the M target image acquisition devices. The number of acquisition devices can be determined in combination with the environment information and the people flow information of the image acquisition area, the accuracy of resource allocation during acquisition is improved, the waste of resources is reduced, and the system performance is improved.

Description

Technical Field

[0001] This application relates to the field of image acquisition and processing technology, specifically to an image acquisition method and related apparatus. Background Technology

[0002] With the continuous development of society, user security is receiving increasing attention. Many public places are equipped with image acquisition devices such as cameras to capture images for monitoring backup or data analysis to identify potential security risks. Existing image acquisition systems operate continuously 24 / 7, but this includes periods of ineffective data collection. For example, multiple devices may still be used for image acquisition when there are few or no people around, leading to excessive operation of the acquisition equipment, increased system consumption, and hindering resource conservation. Summary of the Invention

[0003] This application provides an image acquisition method and related apparatus that can determine the number of acquisition devices by combining environmental information and pedestrian flow information of the image acquisition area, thereby improving the accuracy of resource allocation during acquisition, reducing resource waste, and improving system performance.

[0004] A first aspect of this application provides an image acquisition method applied to an image acquisition system, the image acquisition system including K image acquisition devices, the image acquisition areas of the K image acquisition devices including the same target image acquisition area, the method including:

[0005] Obtain pedestrian traffic and environmental information in the target image acquisition area;

[0006] Based on the pedestrian flow information and the environmental information, M target image acquisition devices are determined from the K image acquisition devices;

[0007] The target image acquisition area is acquired using the M target image acquisition devices.

[0008] In one possible implementation, determining M target image acquisition devices from the K image acquisition devices based on the pedestrian flow information and the environmental information includes:

[0009] Based on the pedestrian flow information, N reference image acquisition devices are determined, and the N reference image acquisition devices are randomly selected from the K image acquisition devices;

[0010] Based on the environmental information, determine the first ambient brightness and the first ambient transparency;

[0011] Based on the first ambient brightness and the first ambient transparency, the M target image acquisition devices are determined from the N reference image acquisition devices.

[0012] In one possible implementation, determining the M target image acquisition devices from the N reference image acquisition devices based on the first ambient brightness and the first ambient transparency includes:

[0013] Obtain the first acquisition influence degree of the first ambient brightness on each of the N reference image acquisition devices to obtain the first acquisition influence degree set;

[0014] Obtain the second acquisition influence degree of the first environmental perspective on each of the N reference image acquisition devices to obtain the second acquisition influence degree set;

[0015] Based on the first acquisition impact set and the second acquisition impact set, determine the target acquisition impact corresponding to each of the N reference image acquisition devices;

[0016] Based on the target acquisition impact degree corresponding to the N reference image acquisition devices, the image quality prediction value corresponding to the N image acquisition devices is determined.

[0017] Based on the image quality prediction values ​​corresponding to the N image acquisition devices, the M target image acquisition devices are determined from the N reference image acquisition devices.

[0018] In one possible implementation, the method further includes:

[0019] Obtain the first occurrence count of the first user in multiple images of the target image acquisition area;

[0020] A first quantity correction value for the target image acquisition device is determined based on the first number of times;

[0021] The number M of the target image acquisition devices is corrected according to the first quantity correction value to obtain the corrected number of the target image acquisition devices.

[0022] In one possible implementation, the method further includes:

[0023] Acquire the first image obtained by the first target image acquisition device from the target image acquisition area;

[0024] The image quality of the first image is obtained, and the first ratio of the user image region to the total image region of the first image is obtained.

[0025] A second quantity correction value is determined based on the image quality and the first ratio value;

[0026] The number M of the target image acquisition devices is corrected according to the second quantity correction value to obtain the corrected number of target image acquisition devices.

[0027] In one possible implementation, after acquiring images of the target image acquisition area using the M target image acquisition devices, the method further includes:

[0028] Acquire images from the target image acquisition area using the M image acquisition devices to obtain a target image set;

[0029] User analysis is performed on the target images in the target image set to obtain multiple user status information;

[0030] If any abnormal status information exists among the multiple user status information, then an alarm information is determined based on the abnormal status information;

[0031] Display the alarm information.

[0032] A second aspect of this application provides an image acquisition device applied to an image acquisition system, the image acquisition system including K image acquisition devices, the image acquisition areas of the K image acquisition devices including the same target image acquisition area, the device comprising:

[0033] The acquisition unit is used to acquire pedestrian traffic information and environmental information of the target image acquisition area;

[0034] The determining unit is used to determine M target image acquisition devices from the K image acquisition devices based on the pedestrian flow information and the environmental information;

[0035] The acquisition unit is used to acquire images of the target image acquisition area through the M target image acquisition devices.

[0036] In one possible implementation, the determining unit is used for:

[0037] Based on the pedestrian flow information, N reference image acquisition devices are determined, and the N reference image acquisition devices are randomly selected from the K image acquisition devices;

[0038] Based on the environmental information, determine the first ambient brightness and the first ambient transparency;

[0039] Based on the first ambient brightness and the first ambient transparency, the M target image acquisition devices are determined from the N reference image acquisition devices.

[0040] In one possible implementation, regarding the determination of the M target image acquisition devices from the N reference image acquisition devices based on the first ambient brightness and the first ambient transparency, the determining unit is configured to:

[0041] Obtain the first acquisition influence degree of the first ambient brightness on each of the N reference image acquisition devices to obtain the first acquisition influence degree set;

[0042] Obtain the second acquisition influence degree of the first environmental perspective on each of the N reference image acquisition devices to obtain the second acquisition influence degree set;

[0043] Based on the first acquisition impact set and the second acquisition impact set, determine the target acquisition impact corresponding to each of the N reference image acquisition devices;

[0044] Based on the target acquisition impact degree corresponding to the N reference image acquisition devices, the image quality prediction value corresponding to the N image acquisition devices is determined.

[0045] Based on the image quality prediction values ​​corresponding to the N image acquisition devices, the M target image acquisition devices are determined from the N reference image acquisition devices.

[0046] In one possible implementation, the device is further used for:

[0047] Obtain the first occurrence count of the first user in multiple images of the target image acquisition area;

[0048] A first quantity correction value for the target image acquisition device is determined based on the first number of times;

[0049] The number M of the target image acquisition devices is corrected according to the first quantity correction value to obtain the corrected number of the target image acquisition devices.

[0050] In one possible implementation, the device is further used for:

[0051] Acquire the first image obtained by the first target image acquisition device from the target image acquisition area;

[0052] The image quality of the first image is obtained, and the first ratio of the user image region to the total image region of the first image is obtained.

[0053] A second quantity correction value is determined based on the image quality and the first ratio value;

[0054] The number M of the target image acquisition devices is corrected according to the second quantity correction value to obtain the corrected number of target image acquisition devices.

[0055] In one possible implementation, after acquiring images of the target image acquisition area using the M target image acquisition devices, the device is further configured to:

[0056] Acquire images from the target image acquisition area using the M image acquisition devices to obtain a target image set;

[0057] User analysis is performed on the target images in the target image set to obtain multiple user status information;

[0058] If any of the multiple user status information is abnormal, then an alarm message is determined based on the abnormal status information.

[0059] Display the alarm information.

[0060] A third aspect of this application provides a terminal including a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to invoke the program instructions to execute the step instructions as described in the first aspect of this application.

[0061] A fourth aspect of this application provides a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the first aspect of this application.

[0062] A fifth aspect of this application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the first aspect of this application. The computer program product may be a software installation package.

[0063] Implementing the embodiments of this application has at least the following beneficial effects:

[0064] By acquiring pedestrian flow information and environmental information of the target image acquisition area, and determining M target image acquisition devices from the K image acquisition devices based on the pedestrian flow information and environmental information, and then using the M target image acquisition devices to acquire images of the target image acquisition area, the number of acquisition devices can be determined by combining the environmental information and pedestrian flow information of the image acquisition area. This improves the accuracy of resource allocation during acquisition, reduces resource waste, and enhances system performance. Attached Figure Description

[0065] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0066] Figure 1 This application provides a schematic diagram of an image acquisition system according to an embodiment of the present application;

[0067] Figure 2 This application provides a flowchart illustrating an image acquisition method.

[0068] Figure 3 A flowchart illustrating another image acquisition method is provided for embodiments of this application;

[0069] Figure 4 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application;

[0070] Figure 5 This application provides a schematic diagram of the structure of an image acquisition device. Detailed Implementation

[0071] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0072] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0073] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0074] To better understand the image acquisition method provided in this application, a brief introduction to the image acquisition system applying the image acquisition method is given below. For example... Figure 1 As shown, the image acquisition system includes K image acquisition devices, such as cameras, specifically used for video surveillance in public places. During video surveillance, images are captured from the monitored area by these devices. Each device can capture a different area depending on its location, or multiple devices can share the same area. Each device has its own acquisition capabilities, such as the number of frames captured per minute and the pixel values ​​of the captured images. These K image acquisition devices can also be referred to as linked devices. A specific method for determining if they are linked devices is based on whether the difference in latitude and longitude is less than a threshold. If it is, the device is considered a linked device. Alternatively, it can be determined by the scene labels of the devices; if the scene labels are the same, they are identified as linked devices. Another method is to use map software to locate the devices based on their geographical location information, determining if they belong to the same business district or building area, such as the same office building or shopping mall. If so, they are identified as linked devices. The aforementioned K image acquisition devices may also include a main image acquisition device, which coordinates and controls the K image acquisition devices, for example, by controlling their shutdown and activation.

[0075] In existing solutions for image acquisition of monitored areas, image acquisition devices within the monitored area typically collect images continuously. However, the number of people in the monitored area varies at different times of day. For example, under normal circumstances, the number of people in the monitored area is very low after midnight. Using multiple devices for image acquisition would waste resources. This application aims to solve this problem by proposing an image acquisition method that combines environmental information and pedestrian flow information of the image acquisition area to determine the number of acquisition devices, improving the accuracy of resource allocation during acquisition, reducing resource waste, and improving system performance. It is understood that in the specific embodiments of this application, data related to user images, user image areas, and user status are involved. When the embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions.

[0076] Please see Figure 2 , Figure 2 This is a flowchart illustrating an image acquisition method provided in an embodiment of this application. Figure 2 As shown, the method is applied to an image acquisition system, which includes K image acquisition devices. The image acquisition areas of the K image acquisition devices include the same target image acquisition area. The method includes:

[0077] 201. Obtain pedestrian traffic information and environmental information of the target image acquisition area.

[0078] The image acquisition device can integrate a thermal imaging device, which can acquire the number of people in the target image acquisition area, thereby obtaining pedestrian flow information. When acquiring environmental information, sensors can be used. Environmental information can include ambient brightness and ambient transparency. Ambient transparency can be understood as the clarity of vision in the current environment; higher visual clarity results in higher ambient transparency, and lower visual clarity results in lower ambient transparency. For example, the presence of fog, water vapor, or smoke in the environment will affect visual clarity. Higher concentrations of fog or smoke result in lower visual clarity, and lower concentrations result in higher visual clarity. Therefore, the concentration of fog, water vapor, or smoke in the current environment can be used to determine visual clarity, and thus ambient transparency.

[0079] Alternatively, one or more of the K image acquisition devices can be used to acquire images of the target image acquisition area. Then, user identification can be performed on the image to obtain the number of people, and the number of people can be divided by the acquisition time to obtain the pedestrian flow information.

[0080] 202. Based on the pedestrian flow information and the environmental information, determine M target image acquisition devices from the K image acquisition devices.

[0081] Based on pedestrian flow information, a subset of image acquisition devices can be identified from K image acquisition devices to form a set of image acquisition devices. Then, based on environmental information, M target image acquisition devices can be selected from this set. This allows for the rapid and accurate identification of the target image acquisition devices. Here, M is an integer less than or equal to K.

[0082] 203. The target image acquisition area is acquired using the M target image acquisition devices.

[0083] M target image acquisition devices can simultaneously acquire images of the target image acquisition area. Because the devices are located at different positions, images of the target image acquisition area can be obtained from different angles. Analyzing and processing these images yields more comprehensive information, improving reliability. Furthermore, by combining environmental and pedestrian traffic information of the image acquisition area to determine the number of acquisition devices, the accuracy of resource allocation during acquisition is improved, resource waste is reduced, and system performance is enhanced.

[0084] In one possible implementation, a method for determining M target image acquisition devices from the K image acquisition devices based on the pedestrian flow information and the environmental information includes:

[0085] A1. Based on the pedestrian flow information, determine N reference image acquisition devices, wherein the N reference image acquisition devices are randomly selected from the K image acquisition devices;

[0086] A2. Based on the environmental information, determine the first ambient brightness and the first ambient transparency;

[0087] A3. Based on the first ambient brightness and the first ambient transparency, determine the M target image acquisition devices from the N reference image acquisition devices.

[0088] Since image acquisition devices have fixed image acquisition capabilities, different capabilities can capture images of different numbers of people. This allows for representation by corresponding to different pedestrian flows; each image acquisition device corresponds to a specific pedestrian flow. Therefore, based on the pedestrian flow information and the corresponding pedestrian flow for each device, N reference image acquisition devices can be determined from the K image acquisition devices. The N reference image acquisition devices are randomly selected from the K image acquisition devices. This can be understood as the N reference image acquisition devices being determined solely based on the sum of their acquisition capabilities and the pedestrian flow information.

[0089] The environmental information includes parameters indicating ambient brightness and parameters indicating ambient transparency. Based on the parameter types, the first ambient brightness and the first ambient transparency can be extracted from the environmental information. Ambient transparency can be understood as the clarity of vision in the current environment; higher vision clarity results in higher ambient transparency, and lower vision clarity results in lower ambient transparency.

[0090] The influence of the environment on the acquisition of each reference image acquisition device can be determined based on the ambient brightness and ambient transparency. Based on this influence, the target image acquisition device can be identified from the reference image acquisition devices, thereby enabling precise confirmation of the target image acquisition device and improving accuracy.

[0091] In one possible implementation, a method for determining the M target image acquisition devices from the N reference image acquisition devices based on the first ambient brightness and the first ambient transparency includes:

[0092] B1. Obtain the first acquisition influence degree of the first ambient brightness on each of the N reference image acquisition devices to obtain the first acquisition influence degree set;

[0093] B2. Obtain the second acquisition influence degree of the first environmental perspective on each of the N reference image acquisition devices to obtain the second acquisition influence degree set;

[0094] B3. Based on the first acquisition impact set and the second acquisition impact set, determine the target acquisition impact corresponding to each of the N reference image acquisition devices;

[0095] B4. Based on the target acquisition impact degree corresponding to the N reference image acquisition devices, determine the image quality prediction value corresponding to the N image acquisition devices respectively;

[0096] B5. Based on the image quality prediction values ​​corresponding to the N image acquisition devices, determine the M target image acquisition devices from the N reference image acquisition devices.

[0097] Here, ambient brightness can be understood as sunlight intensity. The first ambient brightness has different impacts on different reference image acquisition devices. For example, the sensitivity of the reference image acquisition device to sunlight intensity will differ depending on its location. Specifically, the sensitivity of the reference image acquisition device to sunlight will differ depending on whether it is in a backlit position or directly facing the sun. This sensitivity can be understood as the degree to which sunlight affects the image quality when the reference image acquisition device acquires images. Therefore, the first acquisition impact can be determined based on the location of the reference image acquisition device and the current sunlight intensity. M is an integer less than or equal to N.

[0098] The ambient brightness can change with the environment. Since the position of the sun will change, the position of the reference image acquisition device will also change, whether it is in a backlit position or facing the sun.

[0099] Since fog, water vapor, smoke, etc. in the environment can affect the clarity of the image, the first environmental perspective will affect the clarity of the image when the reference image acquisition device acquires the image. The higher the first environmental perspective, the clearer the image and the smaller the second acquisition influence. The lower the first environmental perspective, the blurrier the image and the greater the second acquisition influence. Thus, the set of second acquisition influence can be obtained.

[0100] The sum of the influence values ​​of the reference image acquisition device in the first acquisition influence value set and the second acquisition influence value set is determined as the target acquisition influence value of the reference image acquisition device.

[0101] The predicted image quality value can be determined based on the magnitude of the target acquisition's influence. A greater influence results in a smaller predicted image quality value, and vice versa. In short, a larger predicted image quality value indicates better image quality, while a smaller predicted image quality value indicates worse image quality.

[0102] The first image acquisition device sequence can be obtained by sorting the image quality prediction values ​​corresponding to the N image acquisition devices in ascending order, and the first M reference image acquisition devices in this sequence can be determined as the target image acquisition devices.

[0103] In this example, the influence of the environment on the image acquisition of the reference image acquisition device is determined based on the ambient brightness and ambient transparency. The target image acquisition device is then determined from multiple reference image acquisition devices based on this influence, thereby improving the accuracy of the target image acquisition device determination.

[0104] In one possible implementation, the method further includes:

[0105] C1. Obtain the first occurrence count of the first user in multiple images of the target image acquisition area;

[0106] C2. Determine a first quantity correction value for the target image acquisition device based on the first number of times;

[0107] C3. Correct the number M of the target image acquisition devices according to the first quantity correction value to obtain the corrected number of the target image acquisition devices.

[0108] One method for determining the first occurrence count of the first user can be to perform face recognition on multiple images, obtain the number of faces containing the first user in the multiple images, and determine this number as the first occurrence count. The face recognition on multiple images can employ general face recognition methods, etc.

[0109] If the first quantity is less than the preset quantity, the first quantity correction value can be determined based on the first count. If the first quantity is greater than or equal to the preset value, the first quantity correction value can be a first preset fixed value, which can be 0, etc., set through empirical values ​​or historical data. The method for determining the first quantity correction value based on the first count can be through a linear proportion. If the determined first quantity correction value is not an integer, it can be rounded up. Alternatively, other corresponding relationships can be used to determine the first quantity correction value.

[0110] The method for correcting the quantity M based on the first quantity correction value can be to subtract the first quantity correction value from M to obtain the corrected quantity of the target image acquisition device. In this case, due to the reduction in the number of users, it can be determined that the flow of people in the target image acquisition area is decreasing, thereby reducing the number of devices and saving system overhead and energy consumption.

[0111] In one possible implementation, the number of acquisition devices can be adjusted based on the quality of the images acquired by the image acquisition devices and the proportion of the user's image area, further reducing system overhead. This method specifically includes:

[0112] D1. Obtain the first image obtained by the first target image acquisition device from the target image acquisition area;

[0113] D2. Obtain the image quality of the first image, and obtain the first ratio value of the user image region in the first image to the total image region of the first image;

[0114] D3. Determine a second quantity correction value based on the image quality and the first ratio value;

[0115] D4. Correct the number M of the target image acquisition devices according to the second quantity correction value to obtain the corrected number of the target image acquisition devices.

[0116] The first image can be any one of multiple images obtained by the first target image acquisition device when acquiring images of the target image acquisition area. It can be randomly selected or an image extracted according to certain rules. The first target image acquisition device can be any one of M target image acquisition devices.

[0117] The image quality of the first image can be characterized by its resolution, sharpness, etc. The lower the resolution, the worse the image quality; the higher the resolution, the better the image quality. The higher the sharpness, the worse the image quality; the lower the sharpness, the worse the image quality.

[0118] In the first image, the user image region and the environment region can be identified. The user image region can be determined through image segmentation methods, thereby determining its area. The total image area can be obtained from the image dimensions; therefore, a first proportion of the user image region to the total image area of ​​the first image can be extracted.

[0119] When determining the second quantity correction value, if the image quality is higher than the preset image quality and the first ratio value is higher than the preset ratio value, the second quantity correction value can be a second preset fixed value, which is set through empirical values ​​or historical data; otherwise, the second quantity correction value is determined based on the image quality and the first ratio value. The higher the image quality, the smaller the second quantity correction value; the worse the image quality, the larger the second quantity correction value; the larger the first ratio value, the larger the second quantity correction value; the smaller the first ratio value, the smaller the second quantity correction value.

[0120] The sum of the second quantity correction value and the number M of the target image acquisition devices can be used to determine the corrected number of target image acquisition devices. At this time, due to the decrease in image quality and the increase in the first ratio value, it is necessary to increase the number of target image acquisition devices to meet the image acquisition needs, thereby improving the reliability of image acquisition.

[0121] In one possible implementation, after acquiring images of the target image acquisition area through the M target image acquisition devices, the users in the target image acquisition area can be analyzed. If a user exhibits an abnormal state, such as fainting, illness, or theft, an alarm can be triggered, thereby improving the system's reliability. The specific method is as follows:

[0122] E1. Obtain images acquired from the target image acquisition area by the M image acquisition devices to obtain a target image set;

[0123] E2. Perform user analysis on the target images in the target image set to obtain multiple user status information;

[0124] E3. If there is abnormal status information among the multiple user status information, then an alarm information is determined based on the abnormal status information.

[0125] E4. Display the alarm information.

[0126] One method for user analysis of target images is to: perform action recognition on the user in the target image to obtain the user's action information; compare the user's action information with action information in a preset action information database, and determine the state corresponding to the corresponding action information as the user's state information; if there is abnormal state information in the user's state information, alarm information can be determined based on the abnormal state information. For example, if the abnormal state is that the user is unconscious, the alarm information is a rescue alarm; or if the abnormal state is that the user is stealing, the alarm information is a warning alarm, etc.

[0127] Alarm information can be displayed through voice broadcasts, SMS notifications, or other means.

[0128] In one specific implementation, the pedestrian traffic within the target image acquisition area can be determined based on statistical data over a specific time period. The number of target image acquisition devices within a corresponding time period can then be determined based on this pedestrian traffic. For example, the pedestrian traffic within the target image acquisition area can be statistically analyzed over the past month or half a month, such as traffic from 8:00 AM to 10:00 AM, 10:00 AM to 12:00 PM, 12:00 PM to 1:00 PM, 1:00 PM to 6:00 PM, and 6:00 PM to 7:00 PM. This allows for segmenting pedestrian traffic into different time periods, thereby determining the number of target image acquisition devices. Higher pedestrian traffic necessitates a larger number of devices, and lower pedestrian traffic necessitates a smaller number of devices. This enables some devices to hibernate during off-peak hours, reducing system overhead.

[0129] Please see Figure 3 , Figure 3 This application provides a schematic flowchart of another image acquisition method. For example... Figure 3 As shown, the method is applied to an image acquisition system, which includes K image acquisition devices. The image acquisition areas of the K image acquisition devices include the same target image acquisition area. The method includes:

[0130] 301. Obtain pedestrian traffic information and environmental information of the target image acquisition area;

[0131] 302. Based on the pedestrian flow information, determine N reference image acquisition devices, wherein the N reference image acquisition devices are randomly selected from the K image acquisition devices;

[0132] 303. Based on the environmental information, determine the first ambient brightness and the first ambient transparency;

[0133] 304. Based on the first ambient brightness and the first ambient transparency, determine the M target image acquisition devices from the N reference image acquisition devices;

[0134] 305. The target image acquisition area is acquired using the M target image acquisition devices.

[0135] The influence of the environment on the acquisition of each reference image acquisition device can be determined based on the ambient brightness and ambient transparency. Based on this influence, the target image acquisition device can be identified from the reference image acquisition devices, thereby enabling precise confirmation of the target image acquisition device and improving accuracy.

[0136] For examples consistent with the above embodiments, please refer to... Figure 4 , Figure 4 A schematic diagram of a terminal structure provided in an embodiment of this application is shown in the figure. It includes a processor, an input device, an output device, and a memory. The processor, input device, output device, and memory are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to call the program instructions. The program includes instructions for performing the following steps.

[0137] Obtain pedestrian traffic and environmental information in the target image acquisition area;

[0138] Based on the pedestrian flow information and the environmental information, M target image acquisition devices are determined from the K image acquisition devices;

[0139] The target image acquisition area is acquired using the M target image acquisition devices.

[0140] The above mainly describes the solutions of the embodiments of this application from the perspective of the method execution process. It is understood that, in order to achieve the above functions, the terminal includes the corresponding hardware structure and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments provided herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0141] This application embodiment can divide the terminal into functional units according to the above method example. For example, each function can be divided into a separate functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0142] For those consistent with the above, please refer to Figure 5 , Figure 5 This application provides a schematic diagram of the structure of an image acquisition device according to an embodiment. Figure 5 As shown, an image acquisition system is applied, the image acquisition system including K image acquisition devices, the image acquisition areas of the K image acquisition devices including the same target image acquisition area, the device including:

[0143] Acquisition unit 501 is used to acquire pedestrian traffic information and environmental information of the target image acquisition area;

[0144] The determining unit 502 is used to determine M target image acquisition devices from the K image acquisition devices based on the pedestrian flow information and the environmental information;

[0145] The acquisition unit 503 is used to acquire images of the target image acquisition area through the M target image acquisition devices.

[0146] In one possible implementation, the determining unit 502 is used to:

[0147] Based on the pedestrian flow information, N reference image acquisition devices are determined, and the N reference image acquisition devices are randomly selected from the K image acquisition devices;

[0148] Based on the environmental information, determine the first ambient brightness and the first ambient transparency;

[0149] Based on the first ambient brightness and the first ambient transparency, the M target image acquisition devices are determined from the N reference image acquisition devices.

[0150] In one possible implementation, in determining the M target image acquisition devices from the N reference image acquisition devices based on the first ambient brightness and the first ambient transparency, the determining unit 502 is configured to:

[0151] Obtain the first acquisition influence degree of the first ambient brightness on each of the N reference image acquisition devices to obtain the first acquisition influence degree set;

[0152] Obtain the second acquisition influence degree of the first environmental perspective on each of the N reference image acquisition devices to obtain the second acquisition influence degree set;

[0153] Based on the first acquisition impact set and the second acquisition impact set, determine the target acquisition impact corresponding to each of the N reference image acquisition devices;

[0154] Based on the target acquisition impact degree corresponding to the N reference image acquisition devices, the image quality prediction value corresponding to the N image acquisition devices is determined.

[0155] Based on the image quality prediction values ​​corresponding to the N image acquisition devices, the M target image acquisition devices are determined from the N reference image acquisition devices.

[0156] In one possible implementation, the device is further used for:

[0157] Obtain the first occurrence count of the first user in multiple images of the target image acquisition area;

[0158] A first quantity correction value for the target image acquisition device is determined based on the first number of times;

[0159] The number M of the target image acquisition devices is corrected according to the first quantity correction value to obtain the corrected number of the target image acquisition devices.

[0160] In one possible implementation, the device is further used for:

[0161] Acquire the first image obtained by the first target image acquisition device from the target image acquisition area;

[0162] The image quality of the first image is obtained, and the first ratio of the user image region to the total image region of the first image is obtained.

[0163] A second quantity correction value is determined based on the image quality and the first ratio value;

[0164] The number M of the target image acquisition devices is corrected according to the second quantity correction value to obtain the corrected number of target image acquisition devices.

[0165] In one possible implementation, after acquiring images of the target image acquisition area using the M target image acquisition devices, the device is further configured to:

[0166] Acquire images from the target image acquisition area using the M image acquisition devices to obtain a target image set;

[0167] User analysis is performed on the target images in the target image set to obtain multiple user status information;

[0168] If any of the multiple user status information is abnormal, then an alarm message is determined based on the abnormal status information.

[0169] Display the alarm information

[0170] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the image acquisition methods described in the above method embodiments.

[0171] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps of any of the image acquisition methods described in the above method embodiments.

[0172] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0173] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0174] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0175] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0176] Furthermore, the functional units in the various embodiments of the application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software program module.

[0177] If the integrated unit is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0178] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk, or an optical disk, etc.

[0179] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. An image acquisition method, characterized in that, The method is applied to an image acquisition system, which includes K image acquisition devices, wherein the image acquisition areas of the K image acquisition devices include the same target image acquisition area. Obtain pedestrian traffic and environmental information in the target image acquisition area; Based on the pedestrian flow information and the environmental information, M target image acquisition devices are determined from the K image acquisition devices. Specifically, based on the pedestrian flow information, N reference image acquisition devices are determined, which are randomly selected from the K image acquisition devices. Based on the environmental information, a first ambient brightness and a first ambient transparency are determined. The first acquisition influence degree of the first ambient brightness on each of the N reference image acquisition devices is obtained to obtain a first acquisition influence degree set. The second acquisition influence degree of the first ambient transparency on each of the N reference image acquisition devices is obtained to obtain a second acquisition influence degree set. Based on the first acquisition influence degree set and the second acquisition influence degree set, the target acquisition influence degree corresponding to each of the N reference image acquisition devices is determined. Based on the target acquisition influence degree corresponding to each of the N reference image acquisition devices, the image quality prediction value corresponding to each of the N image acquisition devices is determined. Based on the image quality prediction value corresponding to each of the N image acquisition devices, the M target image acquisition devices are determined from the N reference image acquisition devices. The target image acquisition area is acquired using the M target image acquisition devices.

2. The method of claim 1, wherein, The method further includes: Obtain the first occurrence count of the first user in multiple images of the target image acquisition area; A first quantity correction value for the target image acquisition device is determined based on the first number of times; The number M of the target image acquisition devices is corrected according to the first quantity correction value to obtain the corrected number of the target image acquisition devices.

3. The method according to claim 1, characterized in that, The method further includes: Acquire the first image obtained by the first target image acquisition device from the target image acquisition area; The image quality of the first image is obtained, and the first ratio of the user image region to the total image region of the first image is obtained. A second quantity correction value is determined based on the image quality and the first ratio value; The number M of the target image acquisition devices is corrected according to the second quantity correction value to obtain the corrected number of target image acquisition devices.

4. The method according to any one of claims 1 to 3, characterized in that, After acquiring images of the target image acquisition area using the M target image acquisition devices, the method further includes: Acquire images from the target image acquisition area using the M image acquisition devices to obtain a target image set; User analysis is performed on the target images in the target image set to obtain multiple user status information; If any abnormal status information exists among the multiple user status information, then an alarm information is determined based on the abnormal status information; Display the alarm information.

5. An image acquisition device, characterized in that An image acquisition system is applied to an image acquisition system comprising K image acquisition devices, wherein the image acquisition areas of the K image acquisition devices include the same target image acquisition area, and the device comprises: The acquisition unit is used to acquire pedestrian traffic information and environmental information of the target image acquisition area; The determining unit is configured to: determine M target image acquisition devices from K image acquisition devices based on the pedestrian flow information and the environmental information; specifically, determine N reference image acquisition devices based on the pedestrian flow information, wherein the N reference image acquisition devices are randomly selected from the K image acquisition devices; determine a first ambient brightness and a first ambient transparency based on the environmental information; obtain a first acquisition influence degree of the first ambient brightness on each of the N reference image acquisition devices to obtain a first acquisition influence degree set; obtain a second acquisition influence degree of the first ambient transparency on each of the N reference image acquisition devices to obtain a second acquisition influence degree set; determine the target acquisition influence degree corresponding to each of the N reference image acquisition devices based on the first acquisition influence degree set and the second acquisition influence degree set; determine the image quality prediction value corresponding to each of the N image acquisition devices based on the target acquisition influence degree corresponding to each of the N reference image acquisition devices; and determine the M target image acquisition devices from the N reference image acquisition devices based on the image quality prediction value corresponding to each of the N image acquisition devices. The acquisition unit is used to acquire images of the target image acquisition area through the M target image acquisition devices.

6. A terminal, characterized by comprising: The system includes a processor, an input device, an output device, and a memory, which are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to invoke the program instructions to perform the method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the method as described in any one of claims 1-4.

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