Image collector visual range detection method and device, electronic equipment and medium

Abstract

Embodiments of the present application disclose a kind of image collector visual range detection method, device, electronic equipment and medium.The method comprises: according to the target image obtained by image acquisition to target image collector periphery preset range, determine the occlusion information in target image collector periphery preset range;According to the current size data of the occlusion included in occlusion information and the growth parameter of the occlusion, determine the predicted size data of the occlusion corresponding to preset time;According to predicted size data, determine the occlusion of target image collector visual range by occlusion.The above-mentioned scheme solves the problem that image collector is occluded by occlusion and cannot be found in time, so that image collector cannot provide effective image acquisition image, so as to determine the predicted size data of the occlusion, predict the occlusion of image collector by occlusion, and facilitate to take corresponding measures to eliminate the occlusion of image collector by occlusion.

Description

Technical Field

[0001] This application relates to the field of image technology, and in particular to a method, apparatus, electronic device, and medium for detecting the visible range of an image acquisition device. Background Technology

[0002] With the development of surveillance technology, image acquisition devices for monitoring have been widely used in our living environment and can be seen everywhere. At present, image acquisition devices are not only deployed near main roads, but also on various auxiliary roads, small roads, and around residential areas.

[0003] Because there are currently a large number of image acquisition devices installed, staff do not review the monitoring footage from each device in real time. Instead, they only access the footage from the corresponding device when a specific incident occurs. However, if the image acquisition device is obstructed by an object, it will not be able to capture any valid monitoring footage, and staff will be unable to obtain any effective monitoring information. Summary of the Invention

[0004] This application provides a method, apparatus, electronic device, and medium for detecting the visible range of an image acquisition device, so as to predict the occlusion of the image acquisition device by obstructions, detect them in advance, and take measures.

[0005] In one embodiment, this application provides a method for detecting the visible range of an image acquisition device, the method comprising:

[0006] Based on the target image obtained by acquiring images of a preset range around the target image acquisition device, the information of obstructions within the preset range around the target image acquisition device is determined;

[0007] Based on the current size data of the obstruction included in the obstruction information, and the growth parameters of the obstruction, the predicted size data of the obstruction corresponding to the preset time is determined;

[0008] Based on the predicted size data, the extent to which the occlusion of the occluder affects the visible range of the target image acquisition device is determined.

[0009] In another embodiment, this application also provides an image acquisition device for detecting the visible range of an image acquisition device, the device comprising:

[0010] The occlusion information determination module is used to determine the occlusion information within the preset range around the target image collector based on the target image obtained by image acquisition of the target image collector within a preset range around the target image collector;

[0011] The predicted size data determination module is used to determine the predicted size data of the occluder corresponding to a preset time based on the current size data of the occluder included in the occluder information and the growth parameters of the occluder.

[0012] The occlusion determination module is used to determine the occlusion status of the occupant on the visible range of the target image acquisition device based on the predicted size data.

[0013] In yet another embodiment, this application also provides an electronic device, including: one or more processors;

[0014] Memory, used to store one or more programs;

[0015] When the one or more programs are executed by the one or more processors, the one or more processors implement the image acquisition range detection method according to any one of the embodiments of this application.

[0016] In one embodiment, this application also provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the image acquisition range detection method as described in any one of the embodiments of this application.

[0017] In this embodiment, by acquiring a target image from a preset area surrounding the target image acquisition device, information on occlusions within that preset area is determined, thereby enabling the detection of occlusions within the preset area. Based on the current size data of the occlusion and its growth parameters included in the occlusion information, predicted size data of the occlusion corresponding to a preset time is determined, thus accurately predicting the growth of the occlusion and facilitating early detection. Furthermore, by determining the occlusion extent of the occlusion within the visible range of the target image acquisition device based on the predicted size data, the occlusion status can be known in advance, facilitating early warning and the implementation of corresponding measures to eliminate occlusion and ensure that the target image acquisition device can acquire effective images. Attached Figure Description

[0018] Figure 1 This is a flowchart of an image acquisition device visible range detection method provided in one embodiment of this application;

[0019] Figure 2 This is a schematic diagram of the detection range provided in one embodiment of this application;

[0020] Figure 3 This is a schematic diagram of an obstruction provided in one embodiment of this application;

[0021] Figure 4 This is a schematic diagram illustrating the growth trend of an obstruction according to one embodiment of this application;

[0022] Figure 5 A flowchart of an image acquisition device visible range detection method provided in another embodiment of this application;

[0023] Figure 6 This is a schematic diagram showing the relative position of an image acquisition device according to another embodiment of this application;

[0024] Figure 7 This is a schematic diagram of the area division of the obstruction provided in another embodiment of this application;

[0025] Figure 8 This is a schematic diagram of preset size data provided for another embodiment of this application;

[0026] Figure 9 This is a schematic diagram of the object distance provided for yet another embodiment of this application;

[0027] Figure 10 This is a schematic diagram of the occlusion ratio provided in yet another embodiment of this application;

[0028] Figure 11 This is a schematic diagram of the structure of an image acquisition device for detecting the visible range according to an embodiment of this application;

[0029] Figure 12 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0030] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present application, not the entire structure.

[0031] Figure 1 This is a flowchart illustrating an image acquisition device's view range detection method according to one embodiment of this application. The image acquisition device view range detection method provided in this embodiment is applicable to situations where the view range of an image acquisition device needs to be detected. Typically, this embodiment is applicable to situations where occlusions affect the view range of an image acquisition device. Specifically, this method can be executed by an image acquisition device view range detection device, which can be implemented in software and / or hardware, and can be integrated into an electronic device capable of implementing the image acquisition device view range detection method. See also... Figure 1 The method in this application embodiment specifically includes:

[0032] S110. Based on the target image obtained by acquiring images within a preset range around the target image acquisition device, determine the information of obstructions within the preset range around the target image acquisition device.

[0033] The image acquisition device can be an image acquisition device installed in outdoor locations such as roads, residential areas, and squares for monitoring. Since image acquisition devices in outdoor locations may be obstructed by continuously growing vegetation, it is necessary to detect the visible range of the image acquisition device installed in the outdoor location to promptly identify any obstruction. The image acquisition device used to acquire images within a preset range around the target image acquisition device can be an image acquisition device specifically designed for acquiring images within the preset range around the target image acquisition device, or it can be a high-point image acquisition device whose visible range includes the target image acquisition device. The electronic device executing this embodiment can be an image acquisition device that acquires images within a preset range around the target image acquisition device; this image acquisition device is a smart image acquisition device. The electronic device executing this embodiment can also be other devices independent of the image acquisition device that acquires images within the preset range around the target image acquisition device, such as a smart terminal or a server. After acquiring the target image, the image acquisition device transmits the target image to other devices via wired or wireless means. The preset range can be set according to actual conditions, such as... Figure 2 As shown, the preset range can be set to a circular range with the target image acquisition device as the center or a point in the visible range as the center and a preset value as the radius.

[0034] In this embodiment, image recognition and other processing are performed on the acquired target image to obtain occlusion information in the target image. This occlusion information refers to the actual information of the occluder, not the information displayed in the target image. The occlusion information may include information about at least one occluder; once an occluder is identified in the image recognition process, its information is determined. The occluders here are not limited to those currently obstructing the visible range of the target image acquisition device, but also include occluders existing within a preset range around the target image acquisition device that are not currently obstructing its visible range. Occlusions within the preset range around the target image acquisition device can be, for example, […]. Figure 3 As shown in A, B, and C.

[0035] S120. Based on the current size data of the obstruction included in the obstruction information and the growth parameters of the obstruction, determine the predicted size data of the obstruction corresponding to the preset time.

[0036] The current size data refers to the actual dimensions of the shading object, including its height, width, and coverage area. The growth parameters of the shading object reflect its growth rate, which may differ between different types. For example, some trees are short, have limited branch extension, small coverage area, low growth parameters, and grow slowly, ceasing further growth after reaching a certain point. Conversely, some trees are tall, have strong branch extension, large coverage area, high growth parameters, and grow rapidly; without pruning, they will continue to expand. Furthermore, even for the same type of shading object, growth parameters may vary at different times, in different environments, or in different areas. For instance, some trees may grow rapidly with high growth parameters in the early stages, and grow slowly with low growth parameters in later stages. Some trees, when growing in environments with abundant trees, are blocked by other trees, making it difficult to absorb sufficient sunlight and water, resulting in slower growth and lower growth parameters. Conversely, in environments with fewer trees, they can absorb ample sunlight and water, leading to faster growth and higher growth parameters. Some trees may grow faster and have higher growth parameters in warmer climates, while growing slower and having lower growth parameters in colder climates. Growth parameters can be determined based on the type of tree providing shade and / or in conjunction with information about its growing environment and region, or they can be obtained through real-time monitoring of the tree.

[0037] For example, growth parameters can reflect the growth rate of the obstructing object, that is, the size data of the obstructing object's growth over a certain period of time. Based on the current size data of the obstructing object, the growth parameters, and a preset time, the predicted size data of the obstructing object at the preset time can be determined, thus enabling the prediction of the obstructing object's growth. The preset time can be determined according to actual conditions. For example, it can be set to one day, one week, one month, etc. If the growth parameters of the obstructing object are different at different time stages, it is necessary to determine the corresponding growth parameters based on the current time and the preset time, so that the growth parameters can accurately reflect the growth of the obstructing object from the current time to the preset time, thereby accurately determining the predicted size data.

[0038] For example, such as Figure 4 As shown, assuming Figure 4 The growth trend curve of the shading object can be used to determine the growth parameter F of the shading object at different times, and then the predicted size data of the shading object can be determined according to the following formula:

[0039] K = K1 + T*F

[0040] Where K represents the predicted size of the obstruction, K1 represents the current size of the obstruction, T represents the time difference between the preset time and the current time, and F represents the growth parameters. The current size data and the predicted size data can be the height of the obstruction, the extension length of the obstruction's branches and leaves, etc.

[0041] In this embodiment of the application, the process of determining the growth parameters includes: determining the growth trend information of the occluder based on at least one occluder information determined by target image acquisition at a preset frequency; and determining the growth parameters of the occluder corresponding to a preset time based on the growth trend information of the occluder.

[0042] The preset frequency can be determined based on actual conditions, such as daily or weekly. Target image acquisition is performed at the preset frequency to identify at least one occlusion. Based on this occlusion information, the growth trend of the occlusion at different times can be determined, thus establishing the growth trend information. This growth trend information can be presented in the form of graphs, tables, etc. Based on this growth trend information, the growth parameters of the occlusion at the preset time can be determined, i.e., the growth rate of the occlusion at the preset time. For example, ... Figure 4 As shown, the slope of the growth curve of the shading object over a period of time can be determined based on the growth curve of the shading object, and used as a growth parameter.

[0043] S130. Based on the predicted size data, determine the occlusion status of the occupant on the visible range of the target image acquisition device.

[0044] For example, the predicted size data may include the location, height, and growth range of the occupant at a preset time. Based on the predicted size data, it can be determined whether the occupant will obstruct the target image acquisition device at the preset time, and the extent of obstruction. By determining whether the occupant will obstruct the target image acquisition device and the extent of obstruction at a future preset time, prediction can be achieved. This allows for timely detection of the time and extent of obstruction before it occurs, facilitating timely reminders to staff to take appropriate measures at the right time to prevent obstruction.

[0045] The technical solution in this application embodiment determines occlusion information within a preset range around the target image collector based on the target image acquired by image acquisition of a target image collector within a preset range, thereby achieving the detection of occlusions within the preset range around the target image collector. By determining the predicted size data of the occlusion at a preset time based on the current size data of the occlusion and its growth parameters included in the occlusion information, the growth status of the occlusion can be accurately predicted, facilitating early determination of the occlusion's growth status. Furthermore, by determining the occlusion extent of the occlusion within the visible range of the target image collector based on the predicted size data, the occlusion status can be known in advance, facilitating early warning and the implementation of corresponding measures to eliminate occlusion and ensure that the target image collector can acquire effective images.

[0046] Figure 5 A flowchart illustrating a view range detection method for an image acquisition device according to another embodiment of this application. This application embodiment is a further optimization of the above embodiments; details not described in detail in this application embodiment are provided in the above embodiments. See also... Figure 5 The image acquisition range detection method provided in this application embodiment may include:

[0047] S210. An image is acquired by using a high-point image acquisition device that is higher than the target image acquisition device to acquire images of a preset range around the target image acquisition device, thereby obtaining a target image.

[0048] For example, such as Figure 6 As shown, the height of the high-point image acquisition device A is higher than the height of the target image acquisition device B, and the positions of the high-point image acquisition device A and the target image acquisition device B are within a certain range, so that the target image acquisition device B is within the visible range of the high-point image acquisition device A.

[0049] In this embodiment, the positional relationship between the target image acquisition device and the high-point image acquisition device can be predetermined. For example, the horizontal distance between them can be determined based on their latitude and longitude coordinates, and the actual distance and relative angle between them can be determined based on their height difference. This allows for subsequent determination of the rotation angle and zoom range required for the high-point image acquisition device to acquire images of a preset area around the target image acquisition device, based on the actual distance and relative angle. In this embodiment, the identification, height, latitude and longitude information of the high-point image acquisition device and the target image acquisition device can be statistically analyzed for easy querying and calculation. The statistical format includes, but is not limited to, tables and documents, as shown in Table 1.

[0050] Table 1

[0051]

[0052] S220. Based on the target image and the relative position information between the target image collector and the high-point image collector, determine the information of obstructions within a preset range around the target image collector.

[0053] For example, the target image is only a display of the occluder. The actual size information of the occluder cannot be determined based solely on the target image. Therefore, in this embodiment of the application, by combining the target image and the relative positional relationship between the target image collector and the high point image collector, the information of the occluder within a preset range around the target image collector can be determined, that is, the actual size, position and other information of the occluder can be determined.

[0054] In this embodiment of the application, determining the occlusion information within a preset range around the target image collector based on the target image and the relative position information between the target image collector and the high-point image collector includes: determining the actual current size data of the occlusion in the target image based on the target image and the relative position information between the target image collector and the high-point image collector; and performing image recognition on the target image to determine the type of occlusion.

[0055] For example, the size of the occluder in the target image is not the actual size of the occluder. It needs to be converted based on the relative positional relationship between the target image acquisition device and the high-point image acquisition device to obtain the actual current size of the occluder. Image recognition is performed on the target image to determine the type of occluder, so that it can be further determined whether the occluder will obstruct the target image acquisition device. In this embodiment, information on occluders within a preset range around the target image acquisition device can also be statistically stored, including the identifier of the target image acquisition device, the identifier of the occluder, the type of occluder, and the name of the occluder.

[0056] S230. Based on the current size data of the obstruction included in the obstruction information and the growth parameters of the obstruction, determine the predicted size data of the obstruction corresponding to the preset time.

[0057] In this embodiment of the application, before determining the predicted size data of the occluder corresponding to a preset time based on the current size data of the occluder included in the occluder information and the growth parameters of the occluder, the method further includes: determining the possibility that the occluder may cause occlusion to the target image collector based on the type of occluder in the occluder information; if the occluder is unlikely to cause occlusion to the target image collector, then the occluder is filtered out.

[0058] For example, since certain types of plant obstructions are determined to be small in size and low in height based on current size data, and these types of plant obstructions are unlikely to regrow, they may not obstruct the target image acquisition device. Therefore, based on the type of obstruction in the obstruction information, it can be determined whether the obstruction has the potential to obstruct the target image acquisition device. This allows for initial screening, filtering out obstructions that are unlikely to obstruct the target image acquisition device. These obstructions will not be detected or analyzed further in subsequent detection processes, thus saving unnecessary workload and improving detection efficiency. In this embodiment, the possibility of an obstruction obstructing the target image acquisition device can also be added to the obstruction statistics, as exemplarily shown in Table 2.

[0059] Table 2

[0060]

[0061] S240. Based on the predicted size data, determine the occlusion area of ​​the occlusion object on the visible range of the target image acquisition device.

[0062] For example, the predicted size data may include data such as the location, height, and extent of branch and leaf expansion of the occluder. This data represents the size of the occluder itself. Furthermore, based on this size data, the area of ​​the occluder obstructing the visible range of the target image acquisition device needs to be determined. Figure 3 As shown, occlusion B obstructs the visible range of the target image acquisition device. However, not all occlusions B obstruct the target image acquisition device. Therefore, it is necessary to determine the actual range of occlusion caused by occlusion B on the target image acquisition device based on the predicted size data of occlusion B, that is, to determine the occlusion area of ​​the occlusion object on the visible range of the target image acquisition device.

[0063] S250. Based on the occlusion area and the theoretical visible area of ​​the target image collector, determine the occlusion ratio of the occlusion object on the visible range of the target image collector.

[0064] For example, based on the parameters of the target image acquisition device, the theoretical visible area of ​​the target image acquisition device can be determined. Based on the occlusion area of ​​the obstruction and the theoretical visible area of ​​the target image acquisition device, the occlusion ratio of the obstruction on the visible range of the target image acquisition device can be determined. Then, the occlusion level is determined based on the occlusion ratio. For instance, if the occlusion ratio is greater than a first preset ratio and less than or equal to a second preset ratio, the occlusion level is determined to be low; if the occlusion ratio is greater than the second preset ratio and less than or equal to a third preset ratio, the occlusion level is determined to be medium; and if the occlusion ratio is greater than the third preset ratio, the occlusion level is determined to be high. The first preset ratio is less than the second preset ratio, and the second preset ratio is less than the third preset ratio. Corresponding alarms can be generated based on the occlusion level, thereby providing timely feedback to staff regarding the occlusion situation.

[0065] In this embodiment, the current size data and the predicted size data are the size data of the occluder on the side closer to the target image collector.

[0066] For example, such as Figure 7 As shown, for occlusions A and B, the side closer to the target image acquisition device is designated as side M, and the side farther from the target image acquisition device is designated as side N. The specific boundary can be determined based on the actual situation. For example, the boundary can be parallel to the boundary of the target image acquisition device's visible range that is closest to the occlusion. In actual detection, since occlusions closer to the target image acquisition device generally cause occlusion, while occlusions farther from the target image acquisition device may not cause occlusion, the size data of only the side closer to the target image acquisition device can be analyzed to save workload, perform targeted detection, and improve detection accuracy.

[0067] The technical solution in this application embodiment can acquire target images by using a high-point image acquisition device to capture images of a preset range around the target image acquisition device, thereby determining the information of obstructions. Thus, the preset range around the target image acquisition device is detected by the currently set image acquisition device. By determining the area of ​​obstruction on the visible range of the target image acquisition device, and determining the obstruction ratio based on the ratio of the obstruction area to the theoretical visible area of ​​the target image acquisition device, the obstruction situation of the obstruction on the target image acquisition device can be intuitively determined, and the obstruction situation can be promptly reported to the staff to eliminate the obstruction on the target image acquisition device.

[0068] This application describes a specific application process for detecting occlusion within a preset range around a target image acquisition device. The details are as follows:

[0069] Step 1: Determine the monitoring scene parameters for the high-point image acquisition device.

[0070] Using the high-point image acquisition device as the center and its visible range as the radius, the target image acquisition device within the visible range of the high-point image acquisition device is circled, and the target image acquisition device within the visible range is patrolled and detected at certain time intervals (T).

[0071] The specific method involves obtaining the latitude and longitude information of the target image acquisition device. The high-point image acquisition device can then calculate the rotation angle and zoom range required to locate the target image acquisition device. Table 1 shows the statistics for each high-point image acquisition device and the target image acquisition devices that can be monitored within its line of sight.

[0072] Step 2: Analyze the surrounding monitoring scene for each target image acquisition device.

[0073] Step one identified the target image acquisition devices within the monitoring scene monitored by the high-point image acquisition device. An analysis was then conducted to determine if there was any possibility of obstruction within the monitoring scene corresponding to each target image acquisition device. For example... Figure 3 As shown, plants within a preset range around the target image acquisition device are identified. After adjusting the angle of the high-point image acquisition device to locate the target image acquisition device, images are acquired within the preset range around the target image acquisition device to confirm whether there are plants around the target image acquisition device and the size information of the plants.

[0074] If plants are present, they need to be focused and magnified to take high-resolution pictures, identify the plant species, and then obtain the maximum growth of that plant species, such as maximum height and maximum width. Different species will have different growth cycles, as well as the maximum growth height and range of the plant itself.

[0075] Step 3: Generate a database of information on obstructions within a preset range around each target image acquisition device.

[0076] The plant species obtained in step two allow us to determine whether plants exist within a preset range around each target image acquisition device, the likelihood of these plants obstructing the device, and detailed information about the plants. For example, if the plant species detected as obstructions within the preset range around the target image acquisition device include bamboo, camphor, and dawn redwood, based on the plant species, it can be determined that bamboo, being small and low in height, will not enter the target image acquisition device's field of vision and therefore cannot obstruct it. Camphor and dawn redwood, however, may obstruct the target image acquisition device. An obstruction information database is generated for each target image acquisition device based on its monitoring location information and plant-related information, as shown in Table 2. Plants unlikely to obstruct the target image acquisition device are filtered out and not analyzed in subsequent detection processes.

[0077] Generate a 2D map of the surrounding scene of the target image acquisition device, such as... Figure 3 As shown, where A represents camphor tree, B represents dawn redwood, and C represents bamboo, the corresponding circular range represents the growth range of the plant branches and leaves.

[0078] Step 4: Regular testing.

[0079] Considering that the same plant species may grow differently in different regions (such as the south and the north), and even in the same region, different growing environments (such as light, water, soil, etc.) may result in different growth conditions, it is necessary to conduct regular inspections of plants that are at risk of being shaded in order to obtain more accurate plant growth predictions.

[0080] The system can periodically monitor and analyze the growth and influence range of plants within a preset area around the target image acquisition device at intervals of S days (e.g., every 7 days). This allows for the acquisition of data on the growth of monitored trees within the preset area around the target image acquisition device, which is then recorded and stored. Figure 7 As shown in the figure, M represents the plant area closer to the target image acquisition device, and N represents the plant area farther from the target image acquisition device. Statistical analysis of the data from the M and N sides yielded Table 3.

[0081] Table 3

[0082]

[0083] Step 5: Analyze the occlusion of the target image acquisition device by the occluder.

[0084] For plant A and plant B, the above steps reveal their growth patterns, allowing us to determine the growth trends in height and width for each cycle. Figure 4 As shown.

[0085] like Figure 8 As shown, for plant A, assuming the preset period is T and the growth parameter is F1, we can obtain S1 = K1 + T * F1;

[0086] Where S1 is the predicted size data of plant A, and K1 is the current size data of plant A.

[0087] For plant B, assuming the preset period is T and the growth parameter is F2, we can obtain S2 = K2 + T * F2;

[0088] Where S2 is the predicted size data of plant B, and K2 is the current size data of plant A.

[0089] Step 6: Determine the occlusion ratio J.

[0090] J = Obstructed area / Theoretical visible area

[0091] Wherein, the occlusion area is the area of ​​the plant that obstructs the visible range of the target image collector, and the theoretical visible area is the theoretical visible area of ​​the target image collector.

[0092] The specific process for determining the occlusion ratio includes:

[0093] like Figure 9 As shown, the center point of the tree is P1, and the center point of the target image acquisition device is P2; the radius of the tree closest to the target image acquisition device is S1. The object distance L when occlusion occurs can be calculated:

[0094] L = W * cos(arcsin(S1 / W)).

[0095] like Figure 10 As shown, the theoretical visible area Q1 = m * n, and the occlusion area Q2 = S1 2 *arcos(D / S1)-D*S1*sin(arcos(D / S1)).

[0096] J = Q2 / Q1.

[0097] When the occlusion ratio is greater than J1 and less than or equal to J2, the occlusion level is determined to be low; when the occlusion ratio is greater than J2 and less than or equal to J3, the occlusion level is determined to be medium; and when the occlusion ratio is greater than J3, the occlusion level is determined to be high, where J1 is less than J2 and J2 is less than J3. Alarms are generated based on different occlusion levels. Through analysis and periodic judgment of vegetation within a preset range around the target image acquisition device, the system estimates the possible time that the target image acquisition device may be obstructed and generates a corresponding alarm.

[0098] The solutions provided in this application have the same beneficial effects as the embodiments described above.

[0099] Figure 11 This is a schematic diagram of an image acquisition device for detecting the visible range of an image acquisition unit according to one embodiment of this application. This device is applicable to situations where the visible range of an image acquisition unit needs to be detected. Typically, embodiments of this application are applicable to situations where occlusions affect the visible range of an image acquisition unit. This device can be implemented in software and / or hardware and can be integrated into an electronic device. See also... Figure 11 The device specifically includes:

[0100] The occlusion information determination module 410 is used to determine the occlusion information within the preset range around the target image collector based on the target image obtained by image acquisition of the target image collector within a preset range around the target image collector.

[0101] The predicted size data determination module 420 is used to determine the predicted size data of the occluder corresponding to a preset time based on the current size data of the occluder included in the occluder information and the growth parameters of the occluder.

[0102] The occlusion determination module 430 is used to determine the occlusion status of the occluder on the visible range of the target image acquisition device based on the predicted size data.

[0103] In this embodiment of the application, the obstruction information determination module 410 includes:

[0104] The target image determination unit is used to acquire images of a preset range around the target image acquisition device through a high-point image acquisition device with a height higher than the target image acquisition device, and obtain a target image.

[0105] The information determination unit is used to determine the information of obstructions within a preset range around the target image collector based on the target image and the relative position information between the target image collector and the high point image collector.

[0106] In this embodiment of the application, the information determination unit is specifically used for:

[0107] Based on the target image and the relative position information between the target image acquisition device and the high-point image acquisition device, the actual current size data of the occluder in the target image is determined;

[0108] Image recognition is performed on the target image to determine the type of occlusion.

[0109] In this embodiment of the application, the device further includes:

[0110] The probability determination module is used to determine the probability that the occlusion object will obstruct the target image acquisition device based on the type of occlusion object in the occlusion object information.

[0111] An obstruction filtering module is used to filter out an obstruction if it is unlikely to obstruct the target image acquisition device.

[0112] In this embodiment of the application, the device further includes:

[0113] The growth trend information determination module is used to determine the growth trend information of the occluder based on at least one occluder information determined by target image acquisition at a preset frequency.

[0114] The growth parameter determination module is used to determine the growth parameters of the shading object corresponding to a preset time based on the growth trend information of the shading object.

[0115] In this embodiment of the application, the occlusion determination module 430 includes:

[0116] The occlusion area determination unit is used to determine the occlusion area of ​​the occluder on the visible range of the target image acquisition device based on the predicted size data.

[0117] The occlusion ratio determination unit is used to determine the occlusion ratio of the occlusion object on the visible range of the target image collector based on the occlusion area and the theoretical visible area of ​​the target image collector.

[0118] In this embodiment, the current size data and the predicted size data are the size data of the occluder on the side closer to the target image collector.

[0119] The image acquisition device for detecting the visible range of an image acquisition device provided in this application can execute the image acquisition device for detecting the visible range of an image acquisition device provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of executing the method.

[0120] Figure 12 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Figure 12 A block diagram is shown that is suitable for implementing an exemplary electronic device 512 according to embodiments of this application. Figure 12 The electronic device 512 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0121] like Figure 12 As shown, the electronic device 512 may include: one or more processors 516; and a memory 528 for storing one or more programs, which, when executed by the one or more processors 516, cause the one or more processors 516 to implement the image acquisition range detection method provided in this application embodiment, including:

[0122] Based on the target image obtained by acquiring images of a preset range around the target image acquisition device, the information of obstructions within the preset range around the target image acquisition device is determined;

[0123] Based on the current size data of the obstruction included in the obstruction information, and the growth parameters of the obstruction, the predicted size data of the obstruction corresponding to the preset time is determined;

[0124] Based on the predicted size data, the extent to which the occlusion of the occluder affects the visible range of the target image acquisition device is determined.

[0125] The components of the electronic device 512 may include, but are not limited to: one or more processors 516, memory 528, and bus 518 connecting different device components (including memory 528 and processor 516).

[0126] Bus 518 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Processor ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0127] Electronic device 512 typically includes a variety of computer-readable storage media. These storage media can be any available storage media that can be accessed by electronic device 512, including volatile and non-volatile storage media, removable and non-removable storage media.

[0128] Memory 528 may include computer device readable storage media in the form of volatile memory, such as random access memory (RAM) 530 and / or cache memory 532. Electronic device 512 may further include other removable / non-removable, volatile / non-volatile computer device storage media. By way of example only, storage system 534 may be used to read and write non-removable, non-volatile magnetic storage media (…). Figure 12 Not shown; usually referred to as a "hard drive"). Although Figure 12 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical storage medium) may be provided. In these cases, each drive may be connected to bus 518 via one or more data storage medium interfaces. Memory 528 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0129] A program / utility 540 having a set (at least one) of program modules 542 may be stored, for example, in memory 528. Such program modules 542 include, but are not limited to, operating devices, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 542 typically perform the functions and / or methods described in the embodiments of this application.

[0130] Electronic device 512 can also communicate with one or more external devices 514 and / or display 524, and with one or more devices that enable a user to interact with the electronic device 512, and / or with any device (e.g., network card, modem, etc.) that enables the electronic device 512 to communicate with one or more other computing devices. This communication can be performed via input / output (I / O) interface 522. Furthermore, electronic device 512 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 520. Figure 12 As shown, network adapter 520 communicates with other modules of electronic device 512 via bus 518. It should be understood that, although... Figure 12 As not shown, other hardware and / or software modules may be used in conjunction with electronic device 512, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID devices, tape drives, and data backup storage devices.

[0131] One or more processors 516 execute various functional applications and data processing by running at least one of the other programs among a plurality of programs stored in memory 528, such as implementing an image acquisition range detection method provided in the embodiments of this application.

[0132] One embodiment of this application provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform a view range detection method for an image acquisition device, including:

[0133] Based on the target image obtained by acquiring images of a preset range around the target image acquisition device, the information of obstructions within the preset range around the target image acquisition device is determined;

[0134] Based on the current size data of the obstruction included in the obstruction information, and the growth parameters of the obstruction, the predicted size data of the obstruction corresponding to the preset time is determined;

[0135] Based on the predicted size data, the extent to which the occlusion of the occluder affects the visible range of the target image acquisition device is determined.

[0136] The computer storage medium in this application embodiment can be any combination of one or more computer-readable storage media. The computer-readable storage medium can be a computer-readable signal storage medium or a computer-readable storage medium in general. For example, a computer-readable storage medium can be—but is not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application embodiment, the computer-readable storage medium can be any tangible storage medium containing or storing a program that can be used by or in conjunction with an instruction execution device, apparatus, or device.

[0137] Computer-readable signal storage media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal storage media may also be any computer-readable storage medium other than a computer-readable storage medium that can transmit, propagate, or transfer a program for use by or in connection with an instruction execution device, apparatus, or apparatus.

[0138] Program code contained on a computer-readable storage medium may be transmitted using any suitable storage medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0139] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or device. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0140] Note that the above are merely preferred embodiments and the technical principles employed in this application. Those skilled in the art will understand that this application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of this application, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for detecting the visible range of an image acquisition device, characterized in that, The method includes: Based on the target image obtained by acquiring images of a preset range around the target image acquisition device, the information of obstructions within the preset range around the target image acquisition device is determined; Based on the current size data of the obstruction included in the obstruction information, and the growth parameters of the obstruction, the predicted size data of the obstruction corresponding to the preset time is determined; Based on the predicted size data, determine the degree of occlusion of the occupant on the visible range of the target image acquisition device; The step of determining the occlusion information within the preset range around the target image collector based on the target image obtained by acquiring images of the target image collector within a preset range around the target image collector includes: The target image is obtained by acquiring images of a preset range around the target image acquisition device using a high-point image acquisition device that is higher than the target image acquisition device; the target image acquisition device is within the visual range of the high-point image acquisition device. Based on the target image and the relative position information between the target image acquisition device and the high-point image acquisition device, the information of obstructions within a preset range around the target image acquisition device is determined.

2. The method according to claim 1, characterized in that, Based on the target image and the relative position information between the target image acquisition device and the high-point image acquisition device, information on obstructions within a preset range around the target image acquisition device is determined, including: Based on the target image and the relative position information between the target image acquisition device and the high-point image acquisition device, the actual current size data of the occluder in the target image is determined; Image recognition is performed on the target image to determine the type of occlusion.

3. The method according to claim 1, characterized in that, Before determining the predicted size data of the occluder corresponding to a preset time based on the current size data of the occluder included in the occluder information and the growth parameters of the occluder, the method further includes: Based on the type of occlusion in the occlusion information, determine the likelihood that the occlusion will obstruct the target image acquisition device; If the obstruction is unlikely to obstruct the target image acquisition device, then the obstruction is filtered out.

4. The method according to claim 1, characterized in that, The process of determining the growth parameters includes: Based on at least one occluder information determined by acquiring target images at a preset frequency, the growth trend information of the occluder is determined. Based on the growth trend information of the obstruction, the growth parameters of the obstruction corresponding to the preset time are determined.

5. The method according to claim 1, characterized in that, Based on the predicted size data, the occlusion status of the occupant on the visible range of the target image acquisition device is determined, including: Based on the predicted size data, determine the area of ​​the obstruction that the obstruction covers within the visible range of the target image acquisition device; Based on the occlusion area and the theoretical visible area of ​​the target image acquisition device, the occlusion ratio of the occlusion object on the visible range of the target image acquisition device is determined.

6. The method according to claim 1, characterized in that, The current size data and the predicted size data are the size data of the occluder on the side closer to the target image acquisition device.

7. A field-of-view detection device for an image acquisition device, characterized in that, The device includes: The occlusion information determination module is used to determine the occlusion information within the preset range around the target image collector based on the target image obtained by image acquisition of the target image collector within a preset range around the target image collector; The predicted size data determination module is used to determine the predicted size data of the occluder corresponding to a preset time based on the current size data of the occluder included in the occluder information and the growth parameters of the occluder. The occlusion determination module is used to determine the occlusion status of the occluder on the visible range of the target image acquisition device based on the predicted size data. The obstruction information determination module includes: The target image determination unit is used to acquire images of a preset range around the target image acquisition device through a high-point image acquisition device that is higher than the target image acquisition device, thereby obtaining a target image; the target image acquisition device is within the visual range of the high-point image acquisition device. The information determination unit is used to determine the information of obstructions within a preset range around the target image collector based on the target image and the relative position information between the target image collector and the high-point image collector.

8. An electronic device, characterized in that, The electronic device includes: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the image acquisition range detection method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the image acquisition range detection method as described in any one of claims 1-6.

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