Method and device for positioning target object

Abstract

The embodiments of the present application provide a method and device for positioning a target object. The method includes: obtaining a first image containing a target object captured by a first camera; calculating the first positioning coordinates of the target object in the first image, and the positioning reliability corresponding to the first positioning coordinates, wherein the positioning reliability is used to characterize the positioning accuracy of the first positioning coordinates; when the positioning reliability is less than a predetermined confidence threshold, matching a second camera to obtain a second image containing the target object through the second camera; calculating the second positioning coordinates of the target object in the second image, and using the second positioning coordinates as the target positioning coordinates. The technical solution of the embodiments of the present application can improve the accuracy of positioning the target object.

Description

Technical Field

[0001] The present application relates to the field of computer and communication technology, and in particular to a method and device for locating a target object. Background Art

[0002] In the target positioning scenario, such as the positioning of the target object in the traffic road area, it is usually necessary to take a picture of the target object at the intersection through an ordinary camera to obtain the positioning coordinates of the target object. However, in some cases, due to the limitations of the camera hardware capabilities and the physical field of view environment, the positioning accuracy of the target object at the edge or occluded position of the picture is low. Therefore, how to improve the accuracy of positioning the target object is a technical problem that needs to be solved urgently. Summary of the invention

[0003] The embodiments of the present application provide a method, device, computer-readable medium and electronic device for locating a target object, thereby improving the accuracy of locating the target object at least to a certain extent.

[0004] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0005] According to one aspect of an embodiment of the present application, a method for positioning a target object is provided, including: acquiring a first image including a target object captured by a first camera; calculating a first positioning coordinate of the target object in the first image, and a positioning confidence corresponding to the first positioning coordinate, wherein the positioning confidence is used to characterize the positioning accuracy of the first positioning coordinate; when the positioning confidence is less than a predetermined confidence threshold, matching a second camera to acquire a second image including the target object through the second camera; calculating the second positioning coordinates of the target object in the second image, and using the second positioning coordinates as the target positioning coordinates.

[0006] According to one aspect of an embodiment of the present application, a device for positioning a target object is provided, the device comprising: an acquisition unit, used to acquire a first image captured by a first camera and containing a target object; a first calculation unit, used to calculate a first positioning coordinate of the target object in the first image, and a positioning confidence corresponding to the first positioning coordinate, wherein the positioning confidence is used to characterize the positioning accuracy of the first positioning coordinate; a matching unit, used to match a second camera when the positioning confidence is less than a predetermined confidence threshold, so as to acquire a second image containing the target object through the second camera; a second calculation unit, used to calculate the second positioning coordinate of the target object in the second image, so as to use the second positioning coordinate as the target positioning coordinate.

[0007] In some embodiments of the present application, based on the aforementioned scheme, the device also includes: an identification unit, used to identify the target object in the first image and obtain the target object attributes of the target object; a generation unit, used to generate an attribute label for the target object based on the target object attributes of the target object, and the attribute label is used to identify the target object.

[0008] In some embodiments of the present application, based on the aforementioned scheme, the first calculation unit is configured to: obtain image parameters corresponding to the first image; and calculate the first positioning coordinates of the target object in the first image based on the image parameters.

[0009] In some embodiments of the present application, based on the aforementioned scheme, the image parameters corresponding to the first image include the positioning coordinates of the first camera, and the first calculation unit is configured to: obtain the position of the target object in the first image; calculate the positioning coordinate difference between the target object and the first camera based on the position of the target object in the first image; calculate the first positioning coordinates of the target object in the first image based on the positioning coordinates of the first camera and the positioning coordinate difference between the target object and the first camera.

[0010] In some embodiments of the present application, based on the aforementioned scheme, the first computing unit is configured to: obtain the position of the target object in the first image; and calculate the positioning reliability corresponding to the first positioning coordinate according to the position of the target object in the first image, wherein the positioning reliability is inversely proportional to the length of the position of the target object in the first image from the center position of the image.

[0011] In some embodiments of the present application, based on the aforementioned scheme, the matching unit is configured to: determine a positioning coordinate interval corresponding to the field of view of other cameras except the first camera; and match the second camera among the other cameras except the first camera based on the positioning coordinate interval.

[0012] In some embodiments of the present application, based on the aforementioned scheme, the matching unit is configured to: detect whether the first positioning coordinate falls within the positioning coordinate interval; when the first positioning coordinate falls within the positioning coordinate interval, determine the camera corresponding to the positioning coordinate interval as the matching second camera.

[0013] In some embodiments of the present application, based on the aforementioned scheme, the matching unit is configured to: send an instant status request message to other cameras except the first camera, wherein the instant status request message is used to request the instant status information of the other cameras; obtain the instant status information of the other feedback; and match the second camera among the other cameras except the first camera based on the instant status information.

[0014] In some embodiments of the present application, based on the aforementioned scheme, the second computing unit is configured to: when the number of matched second cameras is at least two, obtain at least two second images containing the target object captured by at least two second cameras; calculate at least two third positioning coordinates of the target object in the at least two second images; perform mathematical statistics on the at least two third positioning coordinates to obtain the second positioning coordinates.

[0015] According to one aspect of an embodiment of the present application, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for locating a target object as described in the above embodiment is implemented.

[0016] According to one aspect of an embodiment of the present application, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method for locating a target object as described in the above embodiments.

[0017] In the technical solutions provided in some embodiments of the present application, the first positioning coordinates of the target object in the first image captured by the first camera and the positioning confidence corresponding to the first positioning coordinates are calculated, and when the positioning confidence is less than a predetermined confidence threshold, the second camera is matched to obtain a second image containing the target object, and the second positioning coordinates of the target object in the second image are calculated again, and the second positioning coordinates are used as the target positioning coordinates. Since the positioning confidence can be used to characterize the positioning accuracy of the first positioning coordinates, the target objects that need to re-capture images to calculate the positioning coordinates can be screened out through a predetermined confidence threshold. Furthermore, the second image of the target object is captured by matching the second camera, and the second positioning coordinates of the target object can be calculated through the second image. Therefore, the technical solutions provided in some embodiments of the present application can improve the accuracy of positioning the target object.

[0018] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0020] Figure 1 A schematic diagram showing an exemplary system architecture to which the technical solution of the embodiments of the present application can be applied;

[0021] Figure 2 An application scenario diagram of a method for locating a target object according to an embodiment of the present application is shown;

[0022] Figure 3 A flow chart showing a method for locating a target object according to an embodiment of the present application is shown;

[0023] Figure 4 A flowchart is shown before calculating the first positioning coordinates of the target object in the first image and the positioning confidence corresponding to the first positioning coordinates according to an embodiment of the present application;

[0024] Figure 5 A detailed flow chart of calculating the first positioning coordinates of the target object in the first image according to an embodiment of the present application is shown;

[0025] Figure 6 A schematic diagram of a monocular vision positioning and ranging principle based on similar triangles according to an embodiment of the present application is shown.

[0026] Figure 7 A display diagram of a target object in a first image captured by a camera according to an embodiment of the present application is shown;

[0027] Figure 8 A flowchart of matching a second camera according to an embodiment of the present application is shown;

[0028] Fig. 9 A detailed flowchart of matching a second camera according to an embodiment of the present application is shown;

[0029] Fig.10 A flowchart of matching a second camera according to an embodiment of the present application is shown;

[0030] Fig.11 A flowchart of calculating the second positioning coordinates of the target object in the second image according to an embodiment of the present application is shown;

[0031] Fig.12 An overall flow chart of positioning a target object in an intersection area based on MEC according to an embodiment of the present application is shown;

[0032] Fig.13 An overall flow chart of positioning a target object in an intersection area based on MEC according to an embodiment of the present application is shown;

[0033] Fig.14 A block diagram showing a target object positioning device according to an embodiment of the present application is shown;

[0034] Fig.15 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0035] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0036] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0037] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0038] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0039] Figure 1 A schematic diagram of an exemplary system architecture to which the technical solution of the embodiments of the present application can be applied is shown.

[0040] like Figure 1 As shown, the system architecture may include terminal devices (such as Figure 1The embodiment of the present invention includes one or more of the smartphone 101, tablet computer 102, and portable computer 103 shown in the figure, and of course, other terminal devices, etc.), a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the terminal device and the server 105. The network 104 may include various connection types, such as a wired communication link, a wireless communication link, etc.

[0041] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. According to the implementation requirements, there may be any number of terminal devices, networks and servers. For example, the server 105 may be a server cluster composed of multiple servers.

[0042] It should be noted that the target object positioning method provided in the embodiment of the present application is generally executed by the server 105, and accordingly, the target object positioning device is generally set in the server 105. However, in other embodiments of the present application, the terminal device may also have similar functions as the server, so as to execute the target object positioning solution provided in the embodiment of the present application.

[0043] It should also be noted that, in addition to being executed by the server 105 or terminal device mentioned above, the target object positioning method provided in the embodiment of the present application can also be executed by a cloud server with cloud computing function.

[0044] Specifically, cloud computing is a computing model that distributes computing tasks on a resource pool composed of a large number of computers, so that various application systems can obtain computing power, storage space and information services as needed. The network that provides resources is called a "cloud". The resources in the "cloud" are infinitely expandable in the eyes of users, and can be obtained at any time, used on demand, and expanded at any time. By establishing a cloud computing resource pool (referred to as a cloud platform, generally referred to as an IaaS (Infrastructure as a Service) platform, various types of virtual resources are deployed in the resource pool for external customers to choose to use. The cloud computing resource pool mainly includes: computing devices (virtualized machines, including operating systems), storage devices, and network devices.

[0045] In one embodiment of the present application, the target object positioning method may be applied in the following scenarios: Figure 2 The scene shown is for locating a target object in an intersection area.

[0046] See also Figure 2 , shows an application scenario diagram of a method for locating a target object according to an embodiment of the present application. Figure 2The intersection area 200 shown includes at least two cameras (such as a first camera and a second camera) and at least two target objects (such as a target object 1 and a target object 2), wherein the target object 1 is a car and the target object 2 is a pedestrian.

[0047] In a specific implementation of this embodiment, first, a first image including a car (target object 1) and a pedestrian (target object 2) captured by a first camera may be obtained, and then the first positioning coordinates of the car and the pedestrian in the first image and the positioning confidence corresponding to the first positioning coordinates of the car and the pedestrian may be calculated. When the positioning confidence is less than a predetermined confidence threshold, a second camera (such as a second camera) may be matched among other cameras except the first camera. Figure 2 ), then obtaining a second image including the car and the pedestrian captured by the second camera, and calculating the second positioning coordinates of the car and the pedestrian in the first image, so as to use the second positioning coordinates as the target positioning coordinates.

[0048] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the objects used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those shown or described.

[0049] The implementation details of the technical solution of the embodiment of the present application are described in detail below:

[0050] According to a first aspect of the present disclosure, a method for positioning a target object is provided.

[0051] See also Figure 3 , which shows a flow chart of a method for locating a target object according to an embodiment of the present application. The method for locating a target object can be executed by a device having a computing and processing function, such as Figure 1 The server 105 shown in FIG. 1 may also be executed by Figure 1 It can be executed by the terminal device shown in , and can also be executed by a cloud server with cloud computing function.

[0052] like Figure 3 As shown, the method for locating the target object at least includes steps 310 to 370:

[0053] Step 310: Acquire a first image including a target object captured by a first camera.

[0054] Step 330 , calculating the first positioning coordinates of the target object in the first image and the positioning reliability corresponding to the first positioning coordinates, wherein the positioning reliability is used to characterize the positioning accuracy of the first positioning coordinates.

[0055] Step 350 , when the positioning confidence is less than a predetermined confidence threshold, matching a second camera to obtain a second image containing the target object through the second camera.

[0056] Step 370: Calculate the second positioning coordinates of the target object in the second image, and use the second positioning coordinates as the target positioning coordinates.

[0057] In the present application, the first camera and the second camera may refer to ordinary camera devices with photo or video recording functions, and the first image, the second image, etc. may be videos or images taken by ordinary camera devices.

[0058] In the present application, the first positioning coordinates and the second positioning coordinates may refer to the three-dimensional coordinates of the target object in an area, or may refer to the longitude, latitude and altitude coordinates of the target object.

[0059] In the present application, it should be noted that when the positioning confidence is not less than a predetermined confidence threshold, the first positioning coordinates are used as the target positioning coordinates of the target object.

[0060] The following is a detailed description of the above steps:

[0061] In step 310, a first image including a target object captured by a first camera is acquired.

[0062] In the present application, the first camera may take a photo or record a video of a target object in a certain area to obtain a first image (picture or video stream) containing the target object. For example, the first camera takes a photo of a target object in an intersection area, and the target object may be a pedestrian, an electric vehicle, a pet, a car, etc.

[0063] In the present application, when the first camera captures the first image containing the target object, the image capture time can also be recorded, that is, the image capture timestamp t1, t2, ..., t n .

[0064] In the present application, the first camera may also have positioning coordinates. Those skilled in the art should know that other cameras except the first camera may also have corresponding positioning coordinates.

[0065] Continue to refer to Figure 3In step 330, the first positioning coordinates of the target object in the first image and the positioning reliability corresponding to the first positioning coordinates are calculated, wherein the positioning reliability is used to characterize the positioning accuracy of the first positioning coordinates.

[0066] In one embodiment of the present application, before calculating the first positioning coordinates of the target object in the first image and the positioning reliability corresponding to the first positioning coordinates, the following may also be implemented: Figure 4 The method shown.

[0067] See also Figure 4 , shows a flow chart before calculating the first positioning coordinates of the target object in the first image and the positioning reliability corresponding to the first positioning coordinates according to an embodiment of the present application, which may specifically include steps 321 to 322:

[0068] Step 321 : identifying a target object in the first image and obtaining target object attributes of the target object.

[0069] In the present application, the target object in the first image can be identified by a visual recognition algorithm, and the visual recognition algorithm can be trained in advance in a supervised or unsupervised manner based on the attribute characteristics (such as shape, outline, size, color, etc.) of various target objects (such as pedestrians, electric vehicles, pets, cars, etc.). Further, the trained visual recognition algorithm can identify the target object in the first image and obtain the attributes of the target object (such as the classification, shape, outline, size, color, etc. of the target object).

[0070] In order to enable those skilled in the art to better understand the technical principle of identifying the target object in the first image, the visual recognition algorithm will be briefly described below in conjunction with the embodiments of the present application:

[0071] Step 1: Use the Selective Search algorithm to extract several candidate regions from the first image.

[0072] Specifically, the Selective Search algorithm may include five steps: (1) determining a region set R containing a number of initialization regions in the first image, and calculating the similarity S = {s1, s2, ...} of each adjacent region in the region set R; (2) finding the adjacent regions with the highest similarity, merging them into new regions, and adding them to R; (3) removing from S all data related to the adjacent regions with the highest similarity in step 2; (4) calculating the similarity between the new region and other adjacent regions; (5) jumping to step 2 until S is empty.

[0073] In (1) above, the similarity between adjacent regions i and j in the region set R can be calculated by the following formula: S(r i ,r j )=a1s colour (r i ,r j )+a2s texture (r i ,r j )+a3s size (r i ,r j )+a4s fill (r i ,r j )

[0074] Among them, s colour (r i ,r j ),s texture (r i ,r j ),s size (r i ,r j ),s fill (r i ,r j ) respectively represent the color similarity, texture similarity, size similarity, and spatial overlap similarity between adjacent regions i and j. Specifically:

[0075] Color similarity: The color space of the first image (image) is converted into three color channels (i.e., hue, saturation, and brightness), and a histogram is calculated for each channel with bins = 25, where the color histogram of each region in the region set R has 25*3 = 75 bins. The color similarity between adjacent regions i and j is calculated using the following formula after the histogram is normalized by dividing it by the region size:

[0076]

[0077] in, Represent the color histogram of region i and region j in the kth interval respectively.

[0078] Texture similarity: Use a Gaussian distribution with a variance of 1 to perform gradient statistics in 8 directions, and then calculate the histogram of the statistical results (the size is consistent with the region size) with bins = 10, and the number of histogram intervals is 8*3*10=240 (using RGB color space). The texture similarity between adjacent regions i and j is calculated by the following formula:

[0079]

[0080] in, Represent the texture histogram of region i and region j in the kth interval respectively.

[0081] Size similarity: In order to ensure that the scale of the region merging operation is relatively uniform, the following formula is used to calculate the size similarity between adjacent regions i and j, with the goal of merging smaller regions first.

[0082]

[0083] Among them, size(im) represents the size of the region (in pixels).

[0084] Spatial overlap similarity: In order to measure the overlap between two regions, the spatial overlap similarity between adjacent regions i and j can be calculated using the following formula:

[0085]

[0086] Step 2: Scale all candidate regions to a fixed size, such as a fixed size of 227×227.

[0087] Step 3: Use (neural network) CNN (including 5 convolutional layers and 2 fully connected layers) to extract the features of the candidate area image and obtain a feature vector of fixed length.

[0088] Step 4: Input the feature vector to a pre-trained SVM classifier to identify the input category and recognize the target object. In the present application, the target object in the first image can be more accurately recognized through the above-mentioned embodiment.

[0089] Step 322: Generate an attribute tag for the target object according to the target object attribute of the target object, wherein the attribute tag is used to identify the target object.

[0090] An attribute label is generated for the target object according to the target object attributes. Specifically, for example, the attributes of the target object are "pedestrian, medium height, short hair, red jacket, black trousers, white shoes", then the attribute label generated for this target object can be an alphabetic label "Pedestrian-medium height-short hair-red jacket-black trousers-white shoes" or a digital label "001-002-003-004-006-007".

[0091] In one embodiment of the present application, the first positioning coordinates of the target object in the first image can be calculated by: Figure 5 The steps shown are implemented.

[0092] See also Figure 5 , shows a detailed flowchart of calculating the first positioning coordinates of the target object in the first image according to an embodiment of the present application, which may specifically include steps 331 to 332:

[0093] Step 331: Obtain image parameters corresponding to the first image.

[0094] Step 332: Calculate first positioning coordinates of the target object in the first image based on the image parameters.

[0095] In a specific implementation of an embodiment, the image parameters corresponding to the first image may include the positioning coordinates of a first camera that captures the first image.

[0096] The specific process of calculating the first positioning coordinates of the target object in the first image may be: first, according to the first image, the position of the target object in the first image is obtained. Then, according to the position of the target object in the first image, the positioning coordinate difference between the target object and the first camera is calculated. Finally, according to the positioning coordinates of the first camera and the positioning coordinate difference between the target object and the first camera, the first positioning coordinates of the target object in the first image are calculated. For example, the positioning coordinate difference between the target object and the first camera is "(△x,△y,△z)=(1,1,1)", and the positioning coordinates of the first camera are "(x1,y1,z1)=(2,2,2)", then the first positioning coordinates of the target object are "(X1,Y1,Z1)=(3,3,3)".

[0097] It should be noted that, in the present application, according to the position of the target object in the first image, the calculation of the positioning coordinate difference between the target object and the first camera can be implemented based on a visual positioning algorithm, which will be referred to below. Figure 6 A brief description of the principle of the visual positioning algorithm:

[0098] like Figure 6 , shows a schematic diagram of a monocular vision positioning principle based on similar triangles according to an embodiment of the present application.

[0099] exist Figure 6 According to the geometric relationship of similar triangles, we can know that

[0100] EG / BD=AG / AD

[0101] GF / DC=AG / AD

[0102] EG / EB=AE / AB

[0103] By passing through the equation, we can know that

[0104] EG / BD=GF / DC=AE / AB

[0105] Furthermore, the positioning coordinate difference between the target object and the camera corresponding to each pixel point can be obtained by measuring equidistant array points (such as a calibration plate) at a higher distance, interpolating, and then scaling up in proportion.

[0106] In a specific implementation of an embodiment, the image parameter corresponding to the first image may also include the image acquisition time when the first camera acquires the first image.

[0107] In the present application, the image acquisition time of the first image can be used to identify the order of the images. In addition, it can also be used to characterize the time characteristics when the target object is at the first positioning coordinates, that is, it can be known when the target object is at the first positioning coordinates.

[0108] In a specific implementation of an embodiment, the image parameters corresponding to the first image may also include an image type identifier of the first image (eg, a video in MP3 format, a picture in JPG format) and an image resolution of the first image (eg, 1920×1080).

[0109] In one embodiment of the present application, the positioning reliability corresponding to the first positioning coordinates can be calculated in the following manner: obtaining the position of the target object in the first image; and calculating the positioning reliability corresponding to the first positioning coordinates based on the position of the target object in the first image, wherein the positioning reliability is inversely proportional to the length of the position of the target object in the first image from the center position of the image.

[0110] In one embodiment of the present application, the positioning reliability corresponding to the first positioning coordinates can also be calculated in the following manner: obtaining the resolution of the first image; and calculating the positioning reliability corresponding to the first positioning coordinates based on the resolution of the first image, wherein the positioning reliability is proportional to the resolution of the first image, that is, the higher the image resolution, the higher the positioning reliability.

[0111] In one embodiment of the present application, the positioning reliability corresponding to the first positioning coordinates can also be calculated in the following manner: obtaining the position of the target object in the first image and the resolution of the first image; calculating the positioning reliability corresponding to the first positioning coordinates according to the position of the target object in the first image and the resolution of the first image, wherein the positioning reliability is negatively correlated with the length of the position of the target object in the first image from the center position of the image, and is positively correlated with the resolution of the first image.

[0112] In order to enable those skilled in the art to better understand the principle of calculating the first positioning coordinates of the target object in the first image and the positioning reliability corresponding to the first positioning coordinates in the present application, the following will refer to Figure 7 To explain:

[0113] See also Figure 7 , shows a display diagram of a target object in a first image captured by a camera according to an embodiment of the present application.

[0114] like Figure 7 As shown, the first image is an image captured by the camera and includes target object 1 and target object 2. In the present application, there is a relative position relationship between the camera and the target object, and the relative position relationship includes the distance between the camera and the target object, the relative angle and orientation, etc.

[0115] because Figure 7 The first image is captured by the first camera, and the first image can reflect the relative position relationship between the camera and the target object. Therefore, the positioning coordinate difference between the target object and the first camera can be calculated based on the relative position relationship between the camera and the target object reflected in the first image. Furthermore, the first positioning coordinates of the target object can be calculated based on the positioning coordinate difference and the positioning coordinates of the first camera.

[0116] In the present application, the positioning reliability corresponding to the first positioning coordinates of the target object can be obtained according to the position of the target object in the first image, such as Figure 7 As shown, due to the influence of the physical field of view of the camera, the target object 2 close to the image center of the first image can usually obtain a high-precision position, while the positioning accuracy of the target object 1 at the edge of the first image is low. Therefore, the positioning reliability corresponding to the first positioning coordinates can be calculated according to the position of the target object in the first image, wherein the positioning reliability is inversely proportional to the length of the position of the target object in the first image from the image center.

[0117] It should be noted that the positioning confidence is used to characterize the degree of positioning accuracy, for example, 0.1 means not very accurate, and 0.99 means very accurate. In addition to being expressed in specific numbers, the positioning confidence can also be expressed in confidence levels. For example, there are 5 levels, 5 means the highest positioning accuracy, and 1 means the lowest positioning accuracy.

[0118] Continue to refer to Figure 3 In step 350, when the positioning confidence is less than a predetermined confidence threshold, a second camera is matched to obtain a second image containing the target object through the second camera.

[0119] In the present application, a confidence threshold is set to filter low-confidence first positioning coordinates. Low confidence means poor positioning accuracy, and secondary positioning is required to improve the accuracy of positioning the target object.

[0120] In one embodiment of the present application, matching the second camera can be performed as follows: Figure 8 The steps shown are implemented.

[0121] See also Figure 8 , shows a flowchart of matching a second camera according to an embodiment of the present application, which may specifically include steps 351 to 352:

[0122] Step 351: determine the positioning coordinate intervals corresponding to the field of view areas of other cameras except the first camera.

[0123] In the present application, there may be at least two cameras including the first camera, for example Figure 2 The intersection area shown includes 4 cameras, each of which has a certain field of view. Furthermore, each of the field of view of a camera has a corresponding positioning coordinate interval. In step 351, the positioning coordinate intervals corresponding to the field of view of other cameras except the first camera are determined.

[0124] Step 352: Match the second camera among cameras other than the first camera based on the positioning coordinate interval.

[0125] In a specific implementation of an embodiment, based on the positioning coordinate interval, matching the second camera among other cameras except the first camera can be performed as follows: Fig. 9 The steps shown are implemented.

[0126] See also Fig. 9 , shows a detailed flow chart of matching the second camera according to an embodiment of the present application, which may specifically include steps 3521 to 3522:

[0127] Step 3521, detecting whether the first positioning coordinates fall within the positioning coordinate interval.

[0128] Step 3522: When the first positioning coordinates fall within the positioning coordinate interval, determine the camera corresponding to the positioning coordinate interval as a matching second camera.

[0129] Furthermore, in the above embodiment, the field of view of the other camera may be a partial field of view of the camera, wherein the partial field of view is the middle part of the entire field of view of the camera. The advantage of this is that since the target object in the middle field of view of the camera will be close to the center of the image in the corresponding image, the positioning coordinates of the target object with higher accuracy can be obtained. Therefore, a better camera can be matched in the above manner, thereby improving the accuracy of positioning the target object.

[0130] In one embodiment of the present application, matching the second camera can be performed as follows: Fig.10 The steps shown are implemented.

[0131] See also Fig.10 , shows a flowchart of matching a second camera according to an embodiment of the present application, which may specifically include steps 353 to 355:

[0132] Step 353: Send an instant status request message to other cameras except the first camera, where the instant status request message is used to request instant status information of other cameras.

[0133] Step 354, obtaining the real-time status information fed back by the other cameras.

[0134] Step 355: Match a second camera from other cameras except the first camera based on the real-time status information.

[0135] In the present application, the real-time status information may include a camera identifier, a real-time field of view image of the camera, and a timestamp corresponding to the real-time field of view image. Furthermore, the real-time status information may also include camera parameter information such as the downward tilt angle of the camera and whether it can be rotated.

[0136] In one embodiment of the present application, before acquiring the second image containing the target object through the second camera, the following solution may also be implemented: sending a control message for acquiring the second image of the target object to the matched second camera.

[0137] Specifically, the control message may include a camera identifier (used to identify the camera for secondary photography), the first positioning coordinates of the target object, the number of target objects, the attribute tags of the target objects (i.e., the basic characteristics of the target objects, used to assist the camera in taking photos, such as focusing), a return cycle (used to indicate the period for the camera to return the second image data), and the image acquisition time of the first image of the target object (used for the camera to perform motion estimation so as to know the camera movement).

[0138] Furthermore, the control message may also include the image format and image resolution of the second image of the target object.

[0139] Continue to refer to Figure 3 In step 370, the second positioning coordinates of the target object in the second image are calculated to use the second positioning coordinates as the target positioning coordinates.

[0140] In one embodiment of the present application, when the number of the matched second cameras is at least two, the second positioning coordinates of the target object in the second image can be calculated by: Fig.11 The steps shown are implemented.

[0141] See also Fig.11 , shows a flowchart of calculating the second positioning coordinates of the target object in the second image according to an embodiment of the present application, which may specifically include steps 371 to 373:

[0142] Step 371: Acquire at least two second images containing the target object captured by at least two second cameras.

[0143] Step 372: Calculate at least two third positioning coordinates of the target object in the at least two second images.

[0144] Step 373: Perform mathematical statistics on the at least two third positioning coordinates to obtain the second positioning coordinates.

[0145] In a specific implementation of an embodiment, performing mathematical statistics on the at least two third positioning coordinates may be averaging the at least two third positioning coordinates to obtain the second positioning coordinates.

[0146] Specifically, for example, when the number of matched second cameras is 3, three second images containing the target object captured by the three second cameras are obtained, and three third positioning coordinates of the target object in the three second images are calculated, where the three third positioning coordinates are "(1.1, 1.1, 1.1), (1.2, 1.2, 1.2), and (1.3, 1.3, 1.3)", respectively. The three third positioning coordinates are averaged to obtain the second positioning coordinates "(1.2, 1.2, 1.2)".

[0147] In a specific implementation of an embodiment, before performing mathematical statistics on the at least two third positioning coordinates, the positioning reliability corresponding to the at least two third positioning coordinates may also be calculated. Further, performing mathematical statistics on the at least two third positioning coordinates may be to determine the third positioning coordinate with the largest positioning reliability among the at least two third positioning coordinates as the second positioning coordinate.

[0148] Specifically, for example, when the number of matched second cameras is 3, three second images containing the target object captured by the three second cameras are obtained, and three third positioning coordinates of the target object in the three second images are calculated, wherein the three third positioning coordinates and the corresponding confidence levels are respectively "[(1.1, 1.1, 1.1), confidence level is 0.91], [(1.2, 1.2, 1.2), confidence level is 0.92], [(1.3, 1.3, 1.3), confidence level is 0.93]", therefore, the second positioning coordinates are "(1.3, 1.3, 1.3).

[0149] In one embodiment of the present application, when the number of the matched second cameras is one, the calculated positioning coordinates may be directly determined as the second positioning coordinates.

[0150] In order to enable those skilled in the art to better understand the present application, the technical solution of the present application will be sorted out from the overall process in combination with the application scenario of locating the target object in the intersection area based on MEC.

[0151] First, we need to explain the concepts of "MEC", "camera" and "target object":

[0152] Target objects: including pedestrians, electric vehicles, cars and other objects.

[0153] Camera: Deployed at intersections, it detects pedestrian and vehicle traffic in real time. It sends the captured images or video streams to MEC for target detection and positioning. It can receive control commands from MEC to achieve secondary photography and positioning of designated targets.

[0154] MEC: In this application, MEC refers to a mobile edge computing platform, which effectively integrates wireless network and Internet technologies, and adds computing, storage, processing and other functions on the wireless network side, builds an open platform to implant applications, and opens the information interaction between the wireless network and the business server through the wireless API, integrates the wireless network and the business, and upgrades the traditional wireless base station to an intelligent base station. In the embodiment of the present application, MEC can be deployed at locations such as intersections, which connect and receive pictures or video streams from cameras at intersections by wired or wireless means, and run visual algorithms to locate targets at intersections. And judge the positioning confidence of each target. For low confidence, it means that its positioning accuracy is poor, select cameras with appropriate fields of view, and send control instructions to take secondary photos and locate such targets.

[0155] See also Fig.12 , shows an overall flow chart of positioning a target object in an intersection area based on MEC according to an embodiment of the present application, and the specific process includes the following:

[0156] Step 1: The first camera 1201 collects image data 1 of a target object at an intersection.

[0157] Step 2: The first camera 1201 sends the collected image data 1 to the MEC.

[0158] Step 3: MEC1202 runs the visual recognition and positioning algorithm to analyze the image data 1.

[0159] Step 4: MEC1202 matches the camera and sends control instructions to collect image data about the target object.

[0160] Step 5: The second camera 1203 collects image data 2 of the target object according to the control instruction.

[0161] Step 6: The second camera 1203 sends the collected image data 2 to the MEC.

[0162] Step 7: MEC1202 runs the visual recognition and positioning algorithm to analyze the image data 2 and obtain the target positioning coordinates of the target object.

[0163] See also Fig.13 , shows an overall flow chart of positioning a target object in an intersection area based on MEC according to an embodiment of the present application, and the specific process includes the following:

[0164] Step 1: The first camera 1301 collects image data 1 of a target object at an intersection.

[0165] Step 2: The first camera 1301 sends the collected image data 1 to the MEC.

[0166] Step 3: MEC1302 runs the visual recognition and positioning algorithm to analyze the image data 1.

[0167] Step 4: MEC1302 requests and obtains the real-time status information of other cameras.

[0168] Step 5: MEC1302 matches the camera according to the real-time status information and sends a control instruction to collect image data about the target object.

[0169] Step 6: The second camera 1303 collects image data 2 of the target object according to the control instruction.

[0170] Step 7: The second camera 1303 sends the collected image data 2 to the MEC.

[0171] Step 8: MEC1302 runs the visual recognition and positioning algorithm to analyze the image data 2 and obtain the target positioning coordinates of the target object.

[0172] In the scene of the intersection area, the above method can be used to obtain high-precision positioning coordinates of target objects such as cars, pedestrians, and electric vehicles, which is conducive to optimizing traffic order. For example, the high-precision positioning coordinates of the target objects can be used to dispatch traffic lights, and to remind drivers that there are people behind them and to pay attention to safety.

[0173] In the technical solutions provided in some embodiments of the present application, the first positioning coordinates of the target object in the first image captured by the first camera and the positioning confidence corresponding to the first positioning coordinates are calculated, and when the positioning confidence is less than a predetermined confidence threshold, the second camera is matched to obtain a second image containing the target object, and the second positioning coordinates of the target object in the second image are calculated again, and the second positioning coordinates are used as the target positioning coordinates. Since the positioning confidence can be used to characterize the positioning accuracy of the first positioning coordinates, the target objects that need to re-capture images to calculate the positioning coordinates can be screened out through a predetermined confidence threshold. Furthermore, the second image of the target object is captured by matching the second camera, and the second positioning coordinates of the target object can be calculated through the second image. Therefore, the technical solutions provided in some embodiments of the present application can improve the accuracy of positioning the target object.

[0174] The following describes an apparatus embodiment of the present application, which can be used to execute the method for locating a target object in the above-mentioned embodiment of the present application. For details not disclosed in the apparatus embodiment of the present application, please refer to the embodiment of the method for locating a target object in the above-mentioned embodiment of the present application.

[0175] Fig.14 A block diagram of a target object positioning device according to an embodiment of the present application is shown.

[0176] Reference Fig.14 , shows a target object positioning device 1400 according to an embodiment of the present application, including: an acquisition unit 1401, a first calculation unit 1402, a matching unit 1403 and a second calculation unit 1404.

[0177] Among them, the acquisition unit 1401 is used to acquire a first image containing a target object captured by a first camera; the first calculation unit 1402 is used to calculate the first positioning coordinates of the target object in the first image, and the positioning confidence corresponding to the first positioning coordinates, wherein the positioning confidence is used to characterize the positioning accuracy of the first positioning coordinates; the matching unit 1403 is used to match the second camera when the positioning confidence is less than a predetermined confidence threshold, so as to acquire a second image containing the target object through the second camera; the second calculation unit 1404 is used to calculate the second positioning coordinates of the target object in the second image, so as to use the second positioning coordinates as the target positioning coordinates.

[0178] In some embodiments of the present application, based on the aforementioned scheme, the device also includes: an identification unit, used to identify the target object in the first image and obtain the target object attributes of the target object; a generation unit, used to generate an attribute label for the target object based on the target object attributes of the target object, and the attribute label is used to identify the target object.

[0179] In some embodiments of the present application, based on the aforementioned scheme, the first calculation unit 1402 is configured to: obtain image parameters corresponding to the first image; and calculate the first positioning coordinates of the target object in the first image based on the image parameters.

[0180] In some embodiments of the present application, based on the aforementioned scheme, the image parameters corresponding to the first image include the positioning coordinates of the first camera, and the first calculation unit 1402 is configured to: obtain the position of the target object in the first image; calculate the positioning coordinate difference between the target object and the first camera based on the position of the target object in the first image; calculate the first positioning coordinates of the target object in the first image based on the positioning coordinates of the first camera and the positioning coordinate difference between the target object and the first camera.

[0181] In some embodiments of the present application, based on the aforementioned scheme, the first calculation unit 1402 is configured to: obtain the position of the target object in the first image; calculate the positioning reliability corresponding to the first positioning coordinate according to the position of the target object in the first image, wherein the positioning reliability is inversely proportional to the length of the position of the target object in the first image from the center position of the image.

[0182] In some embodiments of the present application, based on the aforementioned scheme, the matching unit 1403 is configured to: determine the positioning coordinate interval corresponding to the field of view of other cameras except the first camera; based on the positioning coordinate interval, match the second camera among the other cameras except the first camera.

[0183] In some embodiments of the present application, based on the aforementioned scheme, the matching unit 1403 is configured to: detect whether the first positioning coordinate falls within the positioning coordinate interval; when the first positioning coordinate falls within the positioning coordinate interval, determine the camera corresponding to the positioning coordinate interval as the matching second camera.

[0184] In some embodiments of the present application, based on the aforementioned scheme, the matching unit 1403 is configured to: send an instant status request message to other cameras except the first camera, wherein the instant status request message is used to request the instant status information of the other cameras; obtain the instant status information of the other feedback; and match the second camera among the other cameras except the first camera based on the instant status information.

[0185] In some embodiments of the present application, based on the aforementioned scheme, the second calculation unit 1404 is configured to: when the number of matched second cameras is at least two, obtain at least two second images containing the target object captured by at least two second cameras; calculate at least two third positioning coordinates of the target object in the at least two second images; perform mathematical statistics on the at least two third positioning coordinates to obtain the second positioning coordinates.

[0186] Fig.15 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown.

[0187] It should be noted that Fig.15 The computer system 1500 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0188] like Fig.15 As shown, the computer system 1500 includes a central processing unit (CPU) 1501, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1502 or the program loaded from the storage part 1508 to the random access memory (RAM) 1503, such as executing the method described in the above embodiment. In RAM 1503, various programs and data required for system operation are also stored. CPU 1501, ROM 1502 and RAM 1503 are connected to each other through bus 1504. Input / output (I / O) interface 1505 is also connected to bus 1504.

[0189] The following components are connected to the I / O interface 1505: an input section 1506 including a keyboard, a mouse, etc.; an output section 1507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1508 including a hard disk, etc.; and a communication section 1509 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 1509 performs communication processing via a network such as the Internet. A drive 1510 is also connected to the I / O interface 1505 as needed. A removable medium 1511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1510 as needed so that a computer program read therefrom is installed into the storage section 1508 as needed.

[0190] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 1509, and / or installed from a removable medium 1511. When the computer program is executed by a central processing unit (CPU) 1501, various functions defined in the system of the present application are executed.

[0191] It should be noted that the computer-readable medium shown in the embodiment of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, - but not limited to - an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or at least two wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as a part of a carrier wave, wherein a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0192] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or at least two executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0193] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0194] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or at least two programs, and when the above one or at least two programs are executed by an electronic device, the electronic device implements the method described in the above embodiment.

[0195] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by at least two modules or units.

[0196] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation methods of the present application.

[0197] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.

[0198] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for locating a target object, characterized in that: The method comprises: Acquire a first image including a target object captured by a first camera; Calculating a first positioning coordinate of the target object in the first image and a positioning reliability corresponding to the first positioning coordinate, wherein the positioning reliability is used to characterize the positioning accuracy of the first positioning coordinate; When the positioning confidence is less than a predetermined confidence threshold, matching a second camera to acquire a second image containing the target object through the second camera; Calculating second positioning coordinates of the target object in the second image, and using the second positioning coordinates as the target positioning coordinates; The step of calculating the first positioning coordinates of the target object in the first image includes: Acquire image parameters corresponding to the first image; the image parameters corresponding to the first image include the positioning coordinates of the first camera; Acquire the position of the target object in the first image; Calculating a location coordinate difference between the target object and the first camera according to the position of the target object in the first image; The first positioning coordinates of the target object in the first image are calculated according to the positioning coordinates of the first camera and the positioning coordinate difference between the target object and the first camera.

2. The method according to claim 1, characterized in that Before calculating the first positioning coordinates of the target object in the first image and the positioning reliability corresponding to the first positioning coordinates, the method further includes: Identify a target object in the first image and obtain a target object attribute of the target object; An attribute tag is generated for the target object according to the target object attribute of the target object, and the attribute tag is used to identify the target object.

3. The method according to claim 1, characterized in that Calculating the positioning reliability corresponding to the first positioning coordinates includes: Acquire the position of the target object in the first image; According to the position of the target object in the first image, a positioning reliability corresponding to the first positioning coordinate is calculated, wherein the positioning reliability is inversely proportional to the length of the position of the target object in the first image from the center position of the image.

4. The method according to claim 1, characterized in that: The matching of the second camera includes: Determine the positioning coordinate intervals corresponding to the field of view areas of other cameras except the first camera; Based on the positioning coordinate interval, the second camera is matched among cameras other than the first camera.

5. The method according to claim 4, characterized in that The matching of the second camera among other cameras except the first camera based on the positioning coordinate interval includes: Detecting whether the first positioning coordinates fall within the positioning coordinate interval; When the first positioning coordinates fall within the positioning coordinate interval, a camera corresponding to the positioning coordinate interval is determined as a matching second camera.

6. The method according to claim 1, characterized in that The matching of the second camera includes: Sending an instant status request message to other cameras except the first camera, wherein the instant status request message is used to request instant status information of the other cameras; Obtaining real-time status information fed back by the other cameras; Based on the real-time status information, a second camera is matched among other cameras except the first camera.

7. The method according to any one of claims 1 to 6, characterized in that When the number of the matched second cameras is at least two, the calculating the second positioning coordinates of the target object in the second image includes: Acquire at least two second images including the target object captured by at least two second cameras; Calculating at least two third positioning coordinates of the target object in the at least two second images; Mathematical statistics are performed on the at least two third positioning coordinates to obtain the second positioning coordinates.

8. A device for locating a target object, characterized in that: The device comprises: An acquisition unit, used to acquire a first image including a target object captured by a first camera; A first calculation unit is used to calculate a first positioning coordinate of the target object in the first image, and a positioning reliability corresponding to the first positioning coordinate, wherein the positioning reliability is used to characterize the positioning accuracy of the first positioning coordinate; a matching unit, configured to match a second camera when the positioning confidence is less than a predetermined confidence threshold, so as to obtain a second image containing the target object through the second camera; A second calculation unit is used to calculate second positioning coordinates of the target object in the second image, so as to use the second positioning coordinates as the target positioning coordinates; The step of calculating the first positioning coordinates of the target object in the first image includes: Acquire image parameters corresponding to the first image; the image parameters corresponding to the first image include the positioning coordinates of the first camera; Acquire the position of the target object in the first image; Calculating a location coordinate difference between the target object and the first camera according to the position of the target object in the first image; The first positioning coordinates of the target object in the first image are calculated according to the positioning coordinates of the first camera and the positioning coordinate difference between the target object and the first camera.

9. A computer-readable medium, characterized in that A computer program is stored, and when the computer program is executed by a processor, the method for locating a target object according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: at least one processor; A storage device, used to store at least one program, when the at least one program is executed by the at least one processor, enables the at least one processor to implement the target object positioning method according to any one of claims 1-7.

11. A computer program product, characterized in that The method comprises a computer program carried on a computer-readable medium, and when the computer program is executed by a processor, the method for locating a target object according to any one of claims 1 to 7 is implemented.

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