CN114896445A

CN114896445A - Method and device for determining position of interest point, electronic equipment and storage medium

Info

Publication number: CN114896445A
Application number: CN202210322038.5A
Authority: CN
Inventors: 胡忠铠; 赵光辉
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2022-03-29
Filing date: 2022-03-29
Publication date: 2022-08-12

Abstract

The present disclosure provides a method and an apparatus for determining a location of an interest point, an electronic device and a storage medium, and relates to the technical field of computers, in particular to the technical field of electronic maps, intelligent transportation and artificial intelligence. The implementation scheme is as follows: acquiring multiple groups of acquired data aiming at the same interest point, wherein each group of acquired data in the multiple groups of acquired data comprises a corresponding acquisition position and a corresponding view angle, the view angle is the direction from the acquisition position to the interest point, and the view angles of the multiple groups of acquired data are different; determining a plurality of candidate locations of the point of interest based on the plurality of sets of collected data; and determining a target location of the point of interest based on the plurality of candidate locations.

Description

Method and device for determining position of interest point, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to the field of electronic maps, intelligent transportation, and artificial intelligence technologies, and in particular, to a method and an apparatus for determining a location of a point of interest, an electronic device, a computer-readable storage medium, and a computer program product.

Background

A Point of Interest (POI) is a geographic object that can be abstracted as a Point in a geographic information system, especially some geographic entities closely related to people's lives, such as schools, banks, restaurants, gas stations, hospitals, supermarkets, etc.

The points of interest may be displayed in the electronic map according to their locations. Accordingly, the user may view the points of interest in the electronic map.

The approaches described in this section are not necessarily approaches that have been previously conceived or pursued. Unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Similarly, unless otherwise indicated, the problems mentioned in this section should not be considered as having been acknowledged in any prior art.

Disclosure of Invention

The disclosure provides a method and a device for determining a point of interest position, an electronic device, a computer readable storage medium and a computer program product.

According to an aspect of the present disclosure, there is provided a method for determining a location of a point of interest, including: acquiring multiple groups of acquired data aiming at the same interest point, wherein each group of acquired data in the multiple groups of acquired data comprises a corresponding acquisition position and a corresponding view angle, the view angle is the direction from the acquisition position to the interest point, and the view angles of the multiple groups of acquired data are different; determining a plurality of candidate locations of the point of interest based on the plurality of sets of collected data; and determining a target location of the point of interest based on the plurality of candidate locations.

According to an aspect of the present disclosure, there is provided an apparatus for determining a location of a point of interest, including: the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is configured to acquire multiple sets of acquired data aiming at the same interest point, each set of acquired data in the multiple sets of acquired data comprises a corresponding acquisition position and a corresponding view angle, the view angle is the direction from the acquisition position to the interest point, and the view angles of the multiple sets of acquired data are different from each other; a first determination module configured to determine a plurality of candidate locations for the point of interest based on the plurality of sets of collected data; and a second determination module configured to determine a target location of the point of interest based on the plurality of candidate locations.

According to an aspect of the present disclosure, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor, the memory storing instructions executable by the at least one processor to enable the at least one processor to perform the method.

According to an aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any of the above aspects.

According to an aspect of the disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the above-described method.

According to one or more embodiments of the present disclosure, efficiency and accuracy of point of interest positioning can be improved.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the embodiments and, together with the description, serve to explain the exemplary implementations of the embodiments. The illustrated embodiments are for purposes of illustration only and do not limit the scope of the claims. Throughout the drawings, identical reference numbers designate similar, but not necessarily identical, elements.

FIG. 1 illustrates a schematic diagram of an exemplary system in which various methods described herein may be implemented, according to an embodiment of the present disclosure;

FIG. 2 shows a flow chart of a method of determination of a location of interest location according to an embodiment of the present disclosure;

FIG. 3 illustrates a schematic diagram of determining a plurality of candidate locations according to an embodiment of the disclosure;

FIG. 4 illustrates a schematic diagram of determining a plurality of candidate locations according to further embodiments of the present disclosure;

FIG. 5 shows a schematic diagram of translating a first probability of a candidate location to a same location coordinate point, according to an embodiment of the disclosure;

FIG. 6 shows a schematic diagram of density clustering results according to an embodiment of the present disclosure;

FIG. 7 shows a block diagram of an apparatus for determining a location of interest point according to an embodiment of the present disclosure; and

FIG. 8 illustrates a block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, the timing relationship, or the importance relationship of the elements, and such terms are used only to distinguish one element from another. In some examples, a first element and a second element may refer to the same instance of the element, and in some cases, based on the context, they may also refer to different instances.

The terminology used in the description of the various described examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element may be one or a plurality of. Furthermore, the term "and/or" as used in this disclosure is intended to encompass any and all possible combinations of the listed items.

In the disclosure, the processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the related users are all in accordance with the regulations of related laws and regulations, and do not violate the customs of the public order.

The accuracy of the location of a point of interest in an electronic map directly affects the quality of the electronic map and the user experience with the electronic map. Therefore, it is necessary to find new or changed points of interest in the real world as soon as possible, determine the locations of the new or changed points of interest, and update the new or changed points of interest into the electronic map.

In the related art, it is generally required to acquire an image of a point of interest in the field and then manually label the position coordinates of the point of interest in the image. The processing mode has the disadvantages of large workload, long time consumption and low efficiency. Moreover, the accuracy of the position of the interest point completely depends on the professional literacy of workers, and the marking quality of different workers, even the same worker at different time periods is uneven, so that the accuracy of the position of the interest point is difficult to ensure.

Therefore, the embodiment of the disclosure provides a method for determining a location of a point of interest, which can improve efficiency and accuracy of locating the point of interest.

Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

Fig. 1 illustrates a schematic diagram of an exemplary system 100 in which various methods and apparatus described herein may be implemented in accordance with embodiments of the present disclosure. Referring to fig. 1, the system 100 includes one or

more client devices

101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120.

Client devices

101, 102, 103, 104, 105, and 106 may be configured to execute one or more applications.

In embodiments of the present disclosure, the server 120 may run one or more services or software applications that enable the method of determining a location of interest location to be performed.

In some embodiments, the server 120 may also provide other services or software applications that may include non-virtual environments and virtual environments. In certain embodiments, these services may be provided as web-based services or cloud services, for example, provided to users of

client devices

101, 102, 103, 104, 105, and/or 106 under a software as a service (SaaS) model.

In the configuration shown in fig. 1, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or a combination thereof, which may be executed by one or more processors. A user operating a

client device

101, 102, 103, 104, 105, and/or 106 may, in turn, utilize one or more client applications to interact with the server 120 to take advantage of the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from system 100. Accordingly, fig. 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

The user may navigate using

client devices

101, 102, 103, 104, 105, and/or 106. The client device may provide an interface that enables a user of the client device to interact with the client device. The client device may also output information to the user via the interface. Although fig. 1 depicts only six client devices, those skilled in the art will appreciate that any number of client devices may be supported by the present disclosure.

Client devices

101, 102, 103, 104, 105, and/or 106 may include various types of computer devices, such as portable handheld devices, general purpose computers (such as personal computers and laptops), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, gaming systems, thin clients, various messaging devices, sensors or other sensing devices, and so forth. These computer devices may run various types and versions of software applications and operating systems, such as MICROSOFT Windows, APPLE iOS, UNIX-like operating systems, Linux, or Linux-like operating systems; or include various Mobile operating systems such as MICROSOFT Windows Mobile OS, iOS, Windows Phone, Android. Portable handheld devices may include cellular telephones, smart phones, tablets, Personal Digital Assistants (PDAs), and the like. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. The gaming system may include a variety of handheld gaming devices, internet-enabled gaming devices, and the like. The client device is capable of executing a variety of different applications, such as various Internet-related applications, communication applications (e.g., email applications), Short Message Service (SMS) applications, and may use a variety of communication protocols.

Network 110 may be any type of network known to those skilled in the art that may support data communications using any of a variety of available protocols, including but not limited to TCP/IP, SNA, IPX, etc. By way of example only, one or more networks 110 may be a Local Area Network (LAN), an ethernet-based network, a token ring, a Wide Area Network (WAN), the internet, a virtual network, a Virtual Private Network (VPN), an intranet, an extranet, a Public Switched Telephone Network (PSTN), an infrared network, a wireless network (e.g., bluetooth, Wi-Fi), and/or any combination of these and/or other networks.

The server 120 may include one or more general purpose computers, special purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-end servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and/or combination. The server 120 may include one or more virtual machines running a virtual operating system, or other computing architecture involving virtualization (e.g., one or more flexible pools of logical storage that may be virtualized to maintain virtual storage for the server). In various embodiments, the server 120 may run one or more services or software applications that provide the functionality described below.

The computing units in server 120 may run one or more operating systems including any of the operating systems described above, as well as any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and/or middle tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, and the like.

In some implementations, the server 120 may include one or more applications to analyze and consolidate data feeds and/or event updates received from users of the

client devices

101, 102, 103, 104, 105, and 106. Server 120 may also include one or more applications to display data feeds and/or real-time events via one or more display devices of

client devices

101, 102, 103, 104, 105, and 106.

In some embodiments, the server 120 may be a server of a distributed system, or a server incorporating a blockchain. The server 120 may also be a cloud server, or a smart cloud computing server or a smart cloud host with artificial intelligence technology. The cloud Server is a host product in a cloud computing service system, and is used for solving the defects of high management difficulty and weak service expansibility in the traditional physical host and Virtual Private Server (VPS) service.

The system 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 130 may be used to store information such as music files. The database 130 may reside in various locations. For example, the database used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The database 130 may be of different types. In certain embodiments, the database used by the server 120 may be, for example, a relational database. One or more of these databases may store, update, and retrieve data to and from the database in response to the command.

In some embodiments, one or more of the databases 130 may also be used by applications to store application data. The databases used by the application may be different types of databases, such as key-value stores, object stores, or regular stores supported by a file system.

The system 100 of fig. 1 may be configured and operated in various ways to enable application of the various methods and apparatus described in accordance with the present disclosure.

According to some embodiments, the client device 101 may include an electronic map application in 106, which may provide various services based on an electronic map. Accordingly, the server 120 may be a server corresponding to the electronic map application. The server 120 may determine the location of the point of interest by, for example, executing the method for determining the location of the point of interest according to the embodiment of the present disclosure, and then update the point of interest into the electronic map. In this way, the user can access the electronic map application in the

client device

101 and 106 to implement the map functions related to the point of interest, such as point of interest search, point of interest navigation, and the like.

FIG. 2 shows a flow chart of a method 200 of determining a location of interest location according to an embodiment of the disclosure. The method 200 is typically performed at a server, such as the server 120 shown in fig. 1. In some embodiments, method 200 may also be performed at a client device (e.g.,

client devices

101, 102, 103, 104, 105, and 106 shown in fig. 1). That is, the execution subject of each step of the method 200 may be the server 120 shown in fig. 1, or may be the

client devices

101, 102, 103, 104, 105, and 106 shown in fig. 1.

As shown in FIG. 2, method 200 includes steps S210-S230.

In step S210, multiple sets of collected data for the same interest point are obtained, where each set of collected data in the multiple sets of collected data includes a corresponding collection position and a corresponding view angle, the view angle is a direction from the collection position to the interest point, and the view angles of the multiple sets of collected data are different from each other.

In step S220, a plurality of candidate locations of the point of interest are determined based on the plurality of sets of acquired data.

In step S230, a target location of the point of interest is determined based on the plurality of candidate locations.

According to the embodiment of the disclosure, the target position of the interest point can be automatically determined based on multiple groups of collected data of different viewing angles, and the efficiency of locating the interest point is improved. Moreover, the multiple groups of collected data with different viewing angles can play a role in mutual supplement and mutual verification in the process of locating the interest points, so that the accuracy of locating the interest points is ensured.

In an embodiment of the present disclosure, the acquisition position and the angle of view in each set of acquisition data correspond. The acquisition location may be any location where the point of interest can be visually observed, the viewing angle being the direction from the acquisition location to the point of interest. The viewing angle may be represented by an angle value of 0-359. For example, 0 ° may represent the true north direction, with the viewing angle gradually increasing to 359 ° in the clockwise direction.

There are multiple acquisition modes for acquisition position and view angle.

For example, a user may hold a positioning device (e.g., a satellite positioning module) and a pointing device (e.g., a compass) to view a point of interest. When the user observes the interest point in front, the position coordinate of the current handheld positioning device is the collection position, and the direction collected by the orientation device is the visual angle.

For another example, the user may capture an image including the point of interest through the terminal device, and a position (which may be obtained through a satellite positioning module arranged in the terminal device) where the user captures the image is the acquisition position. When the user takes an image, the current orientation of the terminal device, i.e., the orientation of the camera, i.e., the shooting direction, can be acquired by a direction sensor provided in the terminal device. Based on the shooting direction and the location of the point of interest in the image, the corresponding perspective can be determined.

Also for example, vehicles such as panoramic capture vehicles, user vehicles, etc. may capture images containing points of interest. The position where the vehicle acquires the image (which can be acquired by a satellite positioning module provided in the vehicle) is the acquisition position. When the vehicle acquires an image, the orientation of the vehicle, that is, the orientation of the camera, that is, the shooting direction, may be acquired by a direction sensor provided in the vehicle. Based on the shooting direction and the location of the point of interest in the image, the corresponding perspective can be determined.

In the embodiment of the disclosure, the view angles of the sets of collected data for determining the position of the point of interest are different, so that rich information about the position of the point of interest can be provided, and the accuracy of the positioning of the point of interest is improved.

Further, according to some embodiments, the absolute value of the difference in the viewing angle of any two of the sets of acquired data is greater than the disparity threshold. The disparity threshold may be set to 15 °, for example. The parallax of the multiple groups of collected data is large (larger than the parallax threshold value), the correlation and the redundancy between different collected data can be reduced, the information quantity is improved, and therefore the accuracy of the positioning of the interest points is improved.

There are various embodiments of determining multiple candidate locations of a point of interest based on multiple sets of acquired data, and determining a target location of a point of interest based on multiple candidate locations.

According to some embodiments, in a first implementation, a plurality of first lines of sight corresponding to the plurality of sets of acquisition data, respectively, may be determined, each of the plurality of first lines of sight being a line passing through the respective acquisition location and having a respective view angle; and taking an intersection of any two first sight lines of the plurality of first sight lines as one candidate position of the plurality of candidate positions.

According to the embodiment, the first sight line corresponding to each set of the acquired data can be quickly generated based on the acquisition position and the view angle, and each intersection point of a plurality of first sight lines is taken as a candidate position. Thereby, a plurality of candidate positions can be quickly determined.

Fig. 3 shows a schematic diagram for determining a plurality of candidate positions according to the above embodiment. As shown in FIG. 3, the first set of data is collected at point A, the view angle is from A to B, and the corresponding first line of sight is ray AB. The acquisition position of the second group of acquired data is point C, the visual angle is the direction from C to D, and the corresponding first sight line is a ray CD. The acquisition position of the third group of acquired data is point E, the visual angle is from E to F, and the corresponding first sight is a ray EF. The rays AB, CD and EF intersect with each other pairwise to form three intersection points G, H, I, and the intersection point G, H, I is a candidate position.

After determining a plurality of candidate positions according to the above embodiment, clustering may be performed on the plurality of candidate positions to obtain a clustering result; and determining the target position of the interest point based on the clustering result. Candidate locations that cannot be grouped with other candidate locations tend to be too far out of position. Through clustering, candidate positions with overlarge position deviation can be filtered, and therefore the accuracy of the positioning of the interest points is improved.

In particular, according to some embodiments, a density clustering algorithm, such as DBSCAN, may be employed to cluster the plurality of candidate locations. If the clustering result only comprises one cluster, the confidence coefficient of the current multiple groups of collected data is higher, and the average value of the core points in the cluster is used as the target position of the interest point. The core point refers to a candidate position in which the number of candidate positions in the preset neighborhood is greater than or equal to a number threshold. If the clustering result comprises at least two clusters, the confidence coefficient of the current multi-group data is low, and the error is large, so that the calculation result can be abandoned, and the target position of the interest point is not recalled.

According to other embodiments, a partitional clustering algorithm such as K-means, or the like may be employed to cluster the plurality of candidate locations, and the mean of each candidate location in the target class cluster is taken as the target location of the interest point. The target class cluster refers to the class cluster with the largest number of candidate positions included in the clustering result.

In a second implementation, multiple candidate locations may be determined in conjunction with the probability and the target location may be determined, according to some embodiments. Specifically, based on a plurality of sets of collected data, a plurality of candidate positions of the interest point and respective first probabilities of the candidate positions are determined; and determining a target location of the point of interest based on the plurality of candidate locations and the respective first probabilities of the plurality of candidate locations. Thereby, the influence of the deviation of the original acquisition data on the positioning of the point of interest can be reduced.

According to some embodiments, the plurality of candidate locations and the first probability thereof may be determined by:

for each of the plurality of sets of collected data: determining a plurality of candidate acquisition positions and respective second probabilities of the plurality of candidate acquisition positions based on the probability density function of the corresponding acquisition positions and a preset position deviation; and determining a second sight line corresponding to each candidate acquisition position based on the corresponding view angle and the plurality of candidate acquisition positions, wherein the second sight line is a line passing through the corresponding candidate acquisition position and having the view angle.

Then, an intersection of any two second sight lines in the plurality of second sight lines corresponding to the plurality of sets of collected data is used as one candidate position in the plurality of candidate positions, and a first probability of the candidate position is a product of second probabilities corresponding to the corresponding two second sight lines.

It can be understood that the acquisition position is often deviated due to equipment, environment and the like. According to the above-described embodiment, a series of possible acquisition positions (i.e., candidate acquisition positions) and their second probabilities may be determined by a preset probability density function of the position deviation. The candidate positions of the interest points are determined based on the candidate acquisition positions, and the calculation deviation of the candidate positions caused by the deviation of the acquisition positions can be reduced, so that the accuracy of the positioning of the interest points is improved.

The deviation of the acquisition position from the true position (i.e., the position deviation) is generally in accordance with the normal distribution N (μ, σ) ² ) That is, the preset probability density function of the positional deviation is a normal distribution probability density function. Where μ is a longitude coordinate or a latitude coordinate of the corresponding collection position, and σ is a preset constant, which is a standard deviation of a deviation obtained by counting deviations of the plurality of sample collection positions and a true position (i.e., a true value corresponding to the sample collection position).

It is noted that, in the above-described embodiment, the plurality of candidate acquisition positions includes the acquisition position.

In the above embodiment, the second probability may be a probability directly calculated based on a probability density function of a preset position deviation, or may be a probability obtained by normalizing a probability calculated by the probability density function. For example, the probabilities calculated by the probability density function may each be divided by the maximum probability (i.e., the probability that μ corresponds) to normalize the probabilities to be in the range of 0 to 1. Hereinafter, the probability obtained by the normalization process is referred to as "relative probability".

Fig. 4 shows a schematic diagram for determining a plurality of candidate positions according to the above embodiment.

As shown in FIG. 4, the first set of collected data is collected at point A ₁ The angle of view is from ₁ To B ₁ In the direction of (a). Based on a probability density function (e.g., a normal distribution probability density function) of the preset position deviation, the candidate collection position can be determined as the point a ₁ 、A ₂ 、A ₃ The corresponding second probabilities (relative probabilities) are 1, 0.8, respectively, and the corresponding second lines of sight are rays A, respectively ₁ B ₁ 、A ₂ B ₂ 、A ₃ B ₃ 。

The acquisition position of the second group of acquired data is point C ₁ The angle of view is from C ₁ To D ₁ In the direction of (a). Based on the probability density function of the preset position deviation, the candidate acquisition position can be determined as a point C ₁ 、C ₂ 、C ₃ The corresponding second probabilities (relative probabilities) are 1, 0.8, respectively, and the corresponding second lines of sight are rays C, respectively ₁ D ₁ 、C ₂ D ₂ 、C ₃ D ₃ 。

The acquisition position of the third group of acquired data is point E ₁ The angle of view is from ₁ To F ₁ In the direction of (a). Based on the probability density function of the preset position deviation, the candidate acquisition position can be determined as a point E ₁ 、E ₂ 、E ₃ The corresponding second probabilities (relative probabilities) are 1, 0.8, respectively, and the corresponding second lines of sight are rays E, respectively ₁ F ₁ 、E ₂ F ₂ 、E ₃ F ₃ 。

Ray A ₁ B ₁ 、A ₂ B ₂ 、A ₃ B ₃ 、C ₁ D ₁ 、C ₂ D ₂ 、C ₃ D ₃ 、E ₁ F ₁ 、E ₂ F ₂ 、E ₃ F ₃ Intersection points formed by intersection are all candidate positions. The first probability of the candidate position is the product of the second probabilities corresponding to the respective two rays. For example, candidate location G is represented by ray A ₁ B ₁ And ray C ₁ D ₁ The intersection is formed, and the second probabilities corresponding to the two rays are both 1, so that the first probability of the candidate position G is 1 × 1. Candidate position H is defined by ray C ₃ D ₃ And ray E ₃ F ₃ Are intersected to formThe second probabilities corresponding to the respective rays are all 0.8, and thus the first probability of the candidate position H is 0.8 × 0.8 — 0.64.

According to some embodiments, after determining the plurality of candidate locations and the first probabilities thereof, at least one candidate location having a first probability greater than or equal to a probability threshold may be taken as the at least one target candidate location; determining respective weights of at least one target candidate position based on the respective first probabilities; and taking the weighted summation result of at least one target candidate position as a target position.

According to the above embodiments, by taking the weighted sum result of several candidate positions with the highest probability as the target position of the point of interest, the fast localization of the point of interest can be achieved.

According to other embodiments, after determining the plurality of candidate locations and the first probabilities thereof, the plurality of candidate locations may be clustered based on the respective first probabilities of the plurality of candidate locations to obtain a clustering result; and determining the target position of the interest point based on the clustering result. Candidate locations that cannot be grouped with other candidate locations tend to be too far apart. Through clustering, candidate positions with overlarge position deviation can be filtered, and therefore the accuracy of the positioning of the interest points is improved.

The current clustering algorithm can not cluster data containing probability information generally. To cluster a plurality of candidate locations comprising a first probability, according to some embodiments, for each of the plurality of candidate locations, a first number of coordinate points corresponding to the candidate location may be generated, the first number being a product of the first probability of the candidate location and a preset constant; and clustering a plurality of coordinate points corresponding to the plurality of candidate positions.

According to the above-described embodiment, based on the preset constant, the first probability of the candidate position can be converted into the number (first number) of coordinate points of the same position, thereby facilitating clustering and enabling the clustering result to embody probability information.

Fig. 5 shows a schematic diagram of translating a first probability of a candidate location to a same location coordinate point according to an embodiment of the disclosure. As shown in FIG. 5, the candidate positions A,B. C, D, E have first probabilities of 0.2, 0.4, 0.3, 0.6, 0.2, respectively. Based on a preset constant 10, a candidate position a with a probability of 0.2 can be converted into 2(0.2 × 10) coordinate points a of the same position ₁ -A ₂ Converting the candidate position B with the probability of 0.4 into 4(0.4 x 10) coordinate points B of the same position ₁ -B ₄ Converting the candidate position C with the probability of 0.3 into 3(0.3 x 10) coordinate points C of the same position ₁ -C ₃ Converting the candidate position D with the probability of 0.6 into 6(0.6 x 10) coordinate points D of the same position ₁ -D ₆ Converting the candidate position E with the probability of 0.2 into 2(0.2 x 10) coordinate points E of the same position ₁ -E ₂ 。

After converting each candidate location into a first number of coordinate points, the coordinate points may be clustered and a target location of the point of interest may be determined based on the clustering results.

According to some embodiments, a density clustering algorithm (e.g., DBSCAN, etc.) may be employed to cluster the coordinate points. And in response to determining that the clustering result only comprises one class cluster, taking the average value of all core points in the class cluster as the target position, wherein the core points are coordinate points of which the number of coordinate points in the preset neighborhood is greater than or equal to a number threshold value. And in response to the fact that the clustering result comprises two or more clusters, giving up the calculation result and not recalling the target position of the interest point.

According to the above embodiments, the density clustering algorithm (e.g., DBSCAN) can filter out outliers well and find coordinate points (i.e., core points) that are most likely to be points of interest. The target position of the interest point is determined based on the core point, and the accuracy of the location of the interest point can be improved.

Fig. 6 shows a schematic diagram of density clustering results according to an embodiment of the present disclosure. Based on the density clustering algorithm, as shown in FIG. 6, A ₁ -A ₂ 、B ₁ -B ₄ 、C ₁ -C ₃ 、D ₁ -D ₆ 、E ₁ -E ₂ A total of 17 coordinate points are grouped into a cluster. Wherein, the coordinate point B ₁ -B ₄ 、C ₁ -C ₃ 、D ₁ -D ₆ Is the core point. Accordingly, the coordinates are calculatedPoint B ₁ -B ₄ 、C ₁ -C ₃ 、D ₁ -D ₆ As the target location of the point of interest.

According to other embodiments, coordinate points may be clustered by using a partition clustering algorithm (e.g., K-means, etc.), and the mean value of each coordinate point in a target cluster is used as the target position, where the target cluster is the cluster including the largest number of coordinate points in the clustering result.

According to the embodiment, the partition clustering algorithm generally has high computational efficiency, so that the target position of the interest point can be quickly determined.

According to some embodiments, the method 200 may further comprise the steps of: point of interest detection is performed on the plurality of images to determine a point of interest region in each image. Subsequently, image features of the respective interest point regions are extracted. And then, at least three target images comprising the same interest point are determined based on the distance of the image characteristics of different interest point areas, wherein each set of the multiple sets of the acquired data corresponds to one target image, and the acquisition position is the shooting position of the corresponding target image.

According to the embodiment, the interest points are extracted through the target detection technology and are matched, different images comprising the same interest point can be accurately identified, the accuracy of original data for locating the interest points is improved, and therefore the accuracy of locating the interest points is improved.

According to some embodiments, the plurality of images may be, for example, images captured by a user, a panoramic capture cart, a user vehicle, or the like, containing points of interest. The point of interest area may be, for example, a signboard area of the point of interest. Point-of-interest detection may be accomplished, for example, through target detection algorithms such as Faster-RCNN, Yolo-v5, SSD, and the like. The detected interest point region may be represented by a rectangular bounding box (bounding box), for example.

According to some embodiments, image features of the point of interest region may be extracted, for example, by a neural network.

According to some embodiments, the distance of the image feature may be, for example, a euclidean distance.

According to some embodiments, for the plurality of target images including the same interest point, the shooting direction of each target image, that is, the orientation of the camera when the target image is acquired, may be further acquired. Based on the shooting direction and the position of the point of interest region in the target image, a corresponding viewing angle can be determined.

According to an embodiment of the present disclosure, a device for determining a location of a point of interest is also provided. Fig. 7 shows a block diagram of an apparatus 700 for determining a location of interest point according to an embodiment of the present disclosure. As shown in fig. 7, the apparatus 700 includes:

an obtaining module 710 configured to obtain multiple sets of collected data for the same interest point, where each set of collected data in the multiple sets of collected data includes a corresponding collection position and a corresponding view angle, the view angle is a direction from the collection position to the interest point, and the view angles of the multiple sets of collected data are different from each other;

a first determination module 720 configured to determine a plurality of candidate locations for the point of interest based on the plurality of sets of collected data; and

a second determination module 730 configured to determine a target location of the point of interest based on the plurality of candidate locations.

According to some embodiments, the first determination module 720 is further configured to: determining a plurality of candidate locations of the point of interest and respective first probabilities of the plurality of candidate locations based on the plurality of sets of collected data; and wherein the second determination module 730 is further configured to: determining a target location of the point of interest based on the plurality of candidate locations and the respective first probabilities of the plurality of candidate locations.

According to some embodiments, the first determining module 720 comprises: a first determination unit configured to, for each of the plurality of sets of acquisition data: determining a plurality of candidate acquisition positions and respective second probabilities of the plurality of candidate acquisition positions based on the probability density function of the corresponding acquisition positions and a preset position deviation; determining a second sight line corresponding to each candidate acquisition position based on the corresponding view angle and the candidate acquisition positions, wherein the second sight line is a line passing through the corresponding candidate acquisition position and having the view angle; and a second determining unit configured to take an intersection of any two second sights of a plurality of second sights corresponding to the plurality of sets of the acquired data as one candidate position of the plurality of candidate positions, wherein a first probability of the candidate position is a product of second probabilities corresponding to the corresponding two second sights.

According to some embodiments, the second determining module 730 comprises: a clustering unit configured to cluster the candidate positions based on respective first probabilities of the candidate positions to obtain a clustering result; and a third determination unit configured to determine a target position of the interest point based on the clustering result.

According to some embodiments, the clustering unit comprises: a point acquisition unit configured to generate, for each of the plurality of candidate positions, a first number of coordinate points corresponding to the candidate position, wherein the first number is a product of a first probability of the candidate position and a preset constant; and a point clustering unit configured to cluster a plurality of coordinate points corresponding to the plurality of candidate positions.

It should be understood that the various modules or units of the apparatus 700 shown in fig. 7 may correspond to the various steps in the method 200 described with reference to fig. 2. Thus, the operations, features and advantages described above with respect to method 200 are equally applicable to apparatus 700 and the modules and units comprised thereby. Certain operations, features and advantages may not be described in detail herein for the sake of brevity.

Although specific functionality is discussed above with reference to particular modules, it should be noted that the functionality of the various modules discussed herein may be divided into multiple modules and/or at least some of the functionality of multiple modules may be combined into a single module. For example, the first determination module 720 and the second determination module 730 described above may be combined into a single module in some embodiments.

It should also be appreciated that various techniques may be described herein in the general context of software, hardware elements, or program modules. The various modules described above with respect to fig. 7 may be implemented in hardware or in hardware in combination with software and/or firmware. For example, the modules may be implemented as computer program code/instructions configured to be executed in one or more processors and stored in a computer-readable storage medium. Alternatively, the modules may be implemented as hardware logic/circuitry. For example, in some embodiments, one or more of the modules 710 and 730 may be implemented together in a System on Chip (SoC). The SoC may include an integrated circuit chip (which includes one or more components of a Processor (e.g., a Central Processing Unit (CPU), microcontroller, microprocessor, Digital Signal Processor (DSP), etc.), memory, one or more communication interfaces, and/or other circuitry), and may optionally execute received program code and/or include embedded firmware to perform functions.

According to an embodiment of the present disclosure, there is also provided an electronic apparatus including: at least one processor; and a memory communicatively coupled to the at least one processor, the memory storing instructions executable by the at least one processor to enable the at least one processor to perform the method for determining the location of the point of interest.

There is also provided, in accordance with an embodiment of the present disclosure, a non-transitory computer-readable storage medium having stored thereon computer instructions for causing a computer to perform the above-described method for determining a location of interest.

There is also provided, in accordance with an embodiment of the present disclosure, a computer program product, including a computer program, which when executed by a processor, implements the above-described method of determining a location of a point of interest.

Referring to fig. 8, a block diagram of a structure of an electronic device 800, which may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic device is intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 8, the electronic device 800 includes a computing unit 801 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)802 or a computer program loaded from a storage unit 808 into a Random Access Memory (RAM) 803. In the RAM803, various programs and data required for the operation of the device 800 can also be stored. The calculation unit 801, the ROM802, and the RAM803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.

A number of components in the electronic device 800 are connected to the I/O interface 805, including: an input unit 806, an output unit 807, a storage unit 808, and a communication unit 809. The input unit 806 may be any type of device capable of inputting information to the device 800, and the input unit 806 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device, and may include, but is not limited to, a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and/or a remote control. Output unit 807 can be any type of device capable of presenting information and can include, but is not limited to, a display, speakers, a video/audio output terminal, a vibrator, and/or a printer. The storage unit 808 may include, but is not limited to, a magnetic disk, an optical disk. The communication unit 809 allows devices800 exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chipsets, such as bluetooth ^TM Devices, 802.11 devices, Wi-Fi devices, WiMAX devices, cellular communication devices, and/or the like.

Computing unit 801 may be a variety of general and/or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and the like. The computing unit 801 performs the various methods and processes described above, such as the method 200. For example, in some embodiments, the method 200 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and/or installed onto device 800 via ROM802 and/or communications unit 809. When loaded into RAM803 and executed by computing unit 801, may perform one or more of the steps of method 200 described above. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the method 200 in any other suitable manner (e.g., by way of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be performed in parallel, sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.

Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the above-described methods, systems and apparatus are merely exemplary embodiments or examples and that the scope of the present invention is not limited by these embodiments or examples, but only by the claims as issued and their equivalents. Various elements in the embodiments or examples may be omitted or may be replaced with equivalents thereof. Further, the steps may be performed in an order different from that described in the present disclosure. Further, the various elements in the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced with equivalent elements that appear after the present disclosure.

Claims

1. A method of determining a location of a point of interest, comprising:

acquiring multiple sets of acquired data aiming at the same interest point, wherein each set of acquired data in the multiple sets of acquired data comprises a corresponding acquisition position and a corresponding view angle, the view angle is the direction from the acquisition position to the interest point, and the view angles of the multiple sets of acquired data are different from each other;

determining a plurality of candidate locations of the point of interest based on the plurality of sets of collected data; and

determining a target location of the point of interest based on the plurality of candidate locations.

2. The method of claim 1, wherein determining a plurality of candidate locations for the point of interest based on the plurality of sets of acquired data comprises:

determining a plurality of first sight lines respectively corresponding to the plurality of groups of acquired data, wherein each first sight line in the plurality of first sight lines is a line passing through a corresponding acquisition position and having a corresponding view angle; and

and taking the intersection point of any two first sight lines in the plurality of first sight lines as one candidate position in the plurality of candidate positions.

3. The method of claim 2, wherein determining the target location of the point of interest based on the plurality of candidate locations comprises:

clustering the candidate positions to obtain a clustering result; and

and determining the target position of the interest point based on the clustering result.

4. The method of claim 1, wherein determining a plurality of candidate locations for the point of interest based on the plurality of sets of acquired data comprises: determining a plurality of candidate locations of the point of interest and respective first probabilities of the plurality of candidate locations based on the plurality of sets of collected data;

and wherein determining the target location of the point of interest based on the plurality of candidate locations comprises: determining a target location of the point of interest based on the plurality of candidate locations and the respective first probabilities of the plurality of candidate locations.

5. The method of claim 4, wherein determining, based on the plurality of sets of acquisition data, a plurality of candidate locations of the point of interest and a first probability of each of the plurality of candidate locations comprises:

for each of the plurality of sets of collected data:

determining a plurality of candidate acquisition positions and respective second probabilities of the plurality of candidate acquisition positions based on the probability density function of the corresponding acquisition positions and a preset position deviation;

determining a second sight line corresponding to each candidate acquisition position based on the corresponding view angle and the candidate acquisition positions, wherein the second sight line is a line passing through the corresponding candidate acquisition position and having the view angle; and

and taking the intersection point of any two second sight lines in the plurality of second sight lines corresponding to the plurality of sets of the acquired data as one candidate position in the plurality of candidate positions, wherein the first probability of the candidate position is the product of the second probabilities corresponding to the corresponding two second sight lines.

6. The method of claim 4 or 5, wherein determining the target location of the point of interest based on the plurality of candidate locations and the respective first probabilities of the plurality of candidate locations comprises:

clustering the candidate positions based on the first probabilities of the candidate positions to obtain a clustering result; and

7. The method of claim 6, wherein clustering the plurality of candidate locations based on the respective first probabilities of the plurality of candidate locations comprises:

for each candidate position in the candidate positions, generating a first number of coordinate points corresponding to the candidate position, wherein the first number is the product of a first probability of the candidate position and a preset constant; and

and clustering a plurality of coordinate points corresponding to the candidate positions.

8. The method of claim 7, wherein the clustering is density clustering, and wherein determining the target location of the point of interest based on the clustering results comprises:

in response to determining that the clustering result includes only one class cluster, taking a mean value of core points in the class cluster as the target position, wherein the core points are coordinate points whose number of coordinate points in a preset neighborhood is greater than or equal to a number threshold.

9. The method of claim 7, wherein the clustering is a partitional clustering, and wherein determining the target location of the point of interest based on the clustering results comprises:

and taking the mean value of all coordinate points in a target class cluster as the target position, wherein the target class cluster is the class cluster with the largest number of coordinate points in the clustering result.

10. The method of claim 4 or 5, wherein determining the target location of the point of interest based on the plurality of candidate locations and the respective first probabilities of the plurality of candidate locations comprises:

taking at least one candidate location for which the first probability is greater than or equal to the probability threshold as at least one target candidate location;

determining respective weights for the at least one target candidate location based on the respective first probabilities; and

and taking the weighted summation result of the at least one target candidate position as the target position.

11. The method of any of claims 1-10, wherein an absolute value of a difference in view angles of any two of the sets of acquisition data is greater than a disparity threshold.

12. The method according to any one of claims 1-11, further comprising:

performing interest point detection on the plurality of images to determine an interest point region in each image;

extracting image characteristics of each interest point area; and

determining at least three target images comprising the same interest point based on the distance of the image features of different interest point regions, wherein each set of acquired data in the multiple sets of acquired data corresponds to one target image, and the acquisition position is the shooting position of the corresponding target image.

13. An apparatus for point of interest location determination, comprising:

the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is configured to acquire multiple sets of acquired data aiming at the same interest point, each set of acquired data in the multiple sets of acquired data comprises a corresponding acquisition position and a corresponding view angle, the view angle is the direction from the acquisition position to the interest point, and the view angles of the multiple sets of acquired data are different from each other;

a first determination module configured to determine a plurality of candidate locations for the point of interest based on the plurality of sets of collected data; and

a second determination module configured to determine a target location of the point of interest based on the plurality of candidate locations.

14. The apparatus of claim 13, wherein the first determination module is further configured to: determining a plurality of candidate locations of the point of interest and respective first probabilities of the plurality of candidate locations based on the plurality of sets of collected data;

and wherein the second determination module is further configured to: determining a target location of the point of interest based on the plurality of candidate locations and the respective first probabilities of the plurality of candidate locations.

15. The apparatus of claim 14, wherein the first determining means comprises:

a first determination unit configured to, for each of the plurality of sets of acquisition data:

a second determining unit configured to take an intersection of any two second sights of a plurality of second sights corresponding to the plurality of sets of the acquired data as one candidate position of the plurality of candidate positions, wherein a first probability of the candidate position is a product of second probabilities corresponding to the respective two second sights.

16. The apparatus of claim 14 or 15, wherein the second determining means comprises:

a clustering unit configured to cluster the candidate positions based on respective first probabilities of the candidate positions to obtain a clustering result; and

a third determination unit configured to determine a target position of the interest point based on the clustering result.

17. The apparatus of claim 16, wherein the clustering unit comprises:

a point acquisition unit configured to generate, for each of the plurality of candidate positions, a first number of coordinate points corresponding to the candidate position, wherein the first number is a product of a first probability of the candidate position and a preset constant; and

a point clustering unit configured to cluster a plurality of coordinate points corresponding to the plurality of candidate positions.

18. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein

The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-12.

19. A non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of claims 1-12.

20. A computer program product comprising a computer program, wherein the computer program realizes the method of any one of claims 1-12 when executed by a processor.