CN111709357A

CN111709357A - Method and device for identifying target area, electronic equipment and road side equipment

Info

Publication number: CN111709357A
Application number: CN202010538224.3A
Authority: CN
Inventors: 刘博�
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Apollo Intelligent Connectivity Beijing Technology Co Ltd
Priority date: 2020-06-12
Filing date: 2020-06-12
Publication date: 2020-09-25
Anticipated expiration: 2040-06-12
Also published as: CN111709357B

Abstract

The embodiment of the disclosure relates to a method and a device for identifying a target area, electronic equipment, a computer-readable storage medium and drive test equipment, and relates to the field of intelligent transportation. The method includes determining location information for a marker detected in a current frame of the video. The method also includes determining a number of categories for classifying the identifier. The identifiers are then classified based on the location information and the number of categories. The method further includes determining, as the target region, a region surrounded by the identifiers belonging to the primary category, the number of the identifiers belonging to the primary category being greater than the first threshold number, based on a result of the classification. According to the method and the device, the road condition information can be timely and accurately updated, and the target area information is broadcasted to road side equipment, a cloud platform, vehicles and the like through the vehicle-road cooperation V2X technology, so that reliable data support is provided for path planning of the vehicles, and the user experience is improved.

Description

Method and device for identifying target area, electronic equipment and road side equipment

Technical Field

Embodiments of the present disclosure relate generally to the field of intelligent transportation, and more particularly, to a method, an apparatus, an electronic device, a computer-readable storage medium, and a drive test device for identifying a target area.

Background

For an autonomous vehicle or other general vehicles, great convenience is provided to a user if a target area (e.g., a construction area, etc.) that may block traffic in front can be found early when planning a driving route of the vehicle. However, for an autonomous vehicle, the sensors carried by itself have a limited range of perception; for a common vehicle, the visual distance and the visual angle of a driver are limited. Therefore, the target area on the road surface cannot be accurately determined, which increases the difficulty in subsequent path planning.

Disclosure of Invention

According to an example embodiment of the present disclosure, a scheme for identifying a target area is provided.

In a first aspect of the present disclosure, a method for identifying a target area is provided. The method may include determining location information for a marker detected in a current frame of the video. The method further includes determining a number of categories for classifying the identifier. The method may further include classifying the identifier based on the location information and the number of categories. Furthermore, the method may further comprise determining, as the target region, a region surrounded by the identifiers belonging to the primary category, the number of the identifiers belonging to the primary category being greater than the first threshold number, based on a result of the classification.

In a second aspect of the present disclosure, there is provided an apparatus for identifying a target area, comprising: a location information determination module configured to determine location information of the marker detected in a current frame of the video; a number of categories determination module configured to determine a number of categories for classifying the identifier; a classification module configured to classify the identifier based on the location information and the number of categories; and a target area determination module configured to determine, as the target area, an area surrounded by the identifiers belonging to the primary category, the number of the identifiers belonging to the primary category being greater than a first threshold number, based on a result of the classification.

In a third aspect of the disclosure, an electronic device is provided that includes one or more processors; and storage means for storing the one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out the method according to the first aspect of the disclosure.

In a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements a method according to the first aspect of the present disclosure.

In a fifth aspect of the present disclosure, there is provided a roadside apparatus including the electronic apparatus as described in the third aspect of the present disclosure.

It should be understood that the statements herein reciting aspects are not intended to limit the critical or essential features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements, and wherein:

FIG. 1 illustrates a schematic diagram of an example environment in which embodiments of the present disclosure can be implemented;

FIG. 2 shows a schematic diagram of a monitoring scenario according to an embodiment of the present disclosure;

FIG. 3A shows a schematic diagram of a frame in a surveillance video in accordance with another embodiment of the present disclosure;

FIG. 3B shows a schematic view of the respective identifiers identified in the frame of FIG. 3A in a planar coordinate system;

FIG. 4 shows a flow diagram of a process for identifying a target area, according to an embodiment of the present disclosure;

FIG. 5 shows a block diagram of an apparatus for identifying a target area according to an embodiment of the present disclosure; and

FIG. 6 illustrates a block diagram of a computing device capable of implementing various embodiments of the present disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.

In describing embodiments of the present disclosure, the terms "include" and its derivatives should be interpreted as being inclusive, i.e., "including but not limited to. The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first," "second," and the like may refer to different or the same object. Other explicit and implicit definitions are also possible below.

As mentioned above, autonomous vehicles need to acquire information related to road conditions through roadside sensing devices to generate a more rational driving strategy. It is understood that since construction, damage, collapse, etc. may exist near or on a road, it is important for an autonomous vehicle to identify the target area where such a situation occurs in time and to re-plan the driving route.

There are two main types of conventional identification schemes. The first scheme is manual collection and reporting. This is the most primitive but simple and reliable method. Specifically, a specially-assigned person drives a car to patrol, and when the position of a target area is found, the specific position of the target area is acquired through a total station or a handheld GPS, and is recorded and reported. However, this solution is too costly and inefficient, especially for larger-scale cities, performing a full road condition check takes too long, the update rate is too slow, and real-time updating is not supported.

In order to overcome the defects of the first scheme, the second scheme is that pedestrians and drivers report a mode. That is, the target area is reported by means of pedestrians walking on the road every day or drivers of traveling vehicles. However, the coverage of this scheme cannot be guaranteed. Moreover, the situation of false alarm may exist on roads with fewer vehicles and pedestrians.

In recent years, the unmanned technique gradually exposes the corners of the head. More and more enterprises are beginning to invest in unmanned research and development and production. It is anticipated that partially autonomous vehicles will be present on the road for some future time. How to provide reliable road information for these autonomous vehicles is a problem that is urgently needed to be solved at present.

According to an embodiment of the present disclosure, a scheme of identifying a target area is provided. For example, location information of the identified identifiers may be obtained and clustered using a K-means (Kmeans) algorithm. Identifiers belonging to the same category are likely to be used to surround the target area. Therefore, the road surface information can be updated in real time by reporting the position information and the time stamp of the target area and each cone bucket contained in the target area. In addition, the scheme depends on a road side sensor and an edge computing node in the road side equipment to detect whether a target area exists on the road in real time, and the road side equipment can transmit various information of the detected target area to a cloud platform, other road side equipment, a vehicle and the like through a vehicle-road cooperation v2x technology. In this way, the automatic driving vehicle or other common vehicles can acquire the road information updated in real time, so that great convenience is brought to the planning of the driving path.

Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings. Fig. 1 illustrates a schematic diagram of an example environment 100 in which various embodiments of the present disclosure can be implemented. As shown in FIG. 1, an example environment 100 includes a current frame 110 of surveillance video, a computing device 120, and a target area 130. In addition to the current frame 110, the surveillance video also includes a plurality of previous frames 111.

As shown in fig. 1, to identify a target region, a current frame 110 is input to a computing device 120. In some embodiments, the computing device 120 may be located in the cloud for identifying the identifier for surrounding the target area according to a particular recognition model. It should be understood that "marker" as described herein refers to an object having a distinct topographical feature for identifying a target area (such as, a construction area, a collapsed area, etc.), which may be a cone or a cylindrical barrel with a speckled pattern, or the like. In some embodiments, computing device 120 may include, but is not limited to, a personal computer, a server computer, a hand-held or laptop device, a mobile device (such as a mobile phone, a Personal Digital Assistant (PDA), a media player, etc.), a multiprocessor system, a consumer electronics, a minicomputer, a mainframe computer, a distributed computing environment that includes any of the above systems or devices, and the like.

Through processing by the computing device 120, a particular portion of the current frame 110 may be determined to be the target region 130. For example, each frame in the video may be monitored in real time, and when the number of identifiers in the current frame 110 is found to be greater than or equal to a threshold number, the K means (Kmeans) algorithm is used to cluster the location information of the identifiers. Then, for the identifiers clustered into a category, if the number of the identifiers belonging to the category is greater than the threshold number, the area surrounded by the identifiers belonging to the category is determined as the target area 130. Thus, the road surface information may be updated in real time by reporting the location information and time stamp of the target area 130 and each identifier contained therein. It should be appreciated that the target area 130 is used to indicate an area that a vehicle, including a vehicle, cannot pass through, thereby affecting the planning of a driving route.

Fig. 2 shows a schematic diagram of a monitoring scenario 200 according to an embodiment of the present disclosure. In the monitoring scenario 200, a drive test device including the drive test monitoring apparatus 210 and the computing device 120 exists, and the computing device 120 may be in communication connection with the drive test monitoring apparatus 210 in a wired or wireless manner. As shown in fig. 2, the roadside monitoring unit 210 may be provided at a location such as a monitor for taking a photo of violation for monitoring road surface information of a substantially straight road. It should be understood that the path is usually monitored by a plurality of drive test monitoring apparatuses at different positions, and different monitoring results may be associated and fused by the computing device 120, and finally the target area is determined. The road side device may report various information of the detected target area to the cloud platform through a vehicle-road cooperation v2x technology, or broadcast the information to other road side devices or vehicles.

As shown in fig. 2, the roadside monitoring unit 210 may photograph the markers such as the

pyramid buckets

221, 222. It should be understood that the roadside monitoring device 210 may also photograph moving objects (not shown) such as people, bicycles, motorcycles, and the like. It should also be understood that for ease of discussion, the arrangement shown in fig. 2 of the present disclosure shows only the cone barrels 221, 222. However, the present disclosure is not limited to monitoring the cone barrels 221, 222 using the roadside monitoring device 210, and other markers (e.g., cylindrical barrels with speckle patterns, etc.) that are present in the monitoring scene of the roadside monitoring device 210 to indicate the target area may all be monitored simultaneously by the roadside monitoring device 210.

One frame of the monitoring video captured by the roadside monitoring device 210 in fig. 2 will be described in detail below. Fig. 3A shows a schematic diagram of a frame 300A in surveillance video according to another embodiment of the present disclosure. In FIG. 3A, identifiers 310-1, 310-2, 310-3, 310-4, and 310-5 (hereinafter collectively 310) are present in frame 300A. It is generally straightforward for a person to determine that a target region consisting of the identifiers 310-1, 310-2, 310-3, 310-4 is present in the frame 300A, and that the identifier 310-5 is isolated from the target region. However, to conserve human resources, the computing device 120 is configured to assume the task of identifying the target area.

To detect the identifier 310 from the frame 300A, the computing device 120 first identifies the identifier 310 in the frame 300A. Fig. 3B is a schematic diagram showing a positional relationship diagram 300B of each marker identified in the frame of fig. 3A in a planar coordinate system. It is to be understood that the positional relationship diagram 300B is obtained by two-dimensionally spatially converting the frame 300A. In fig. 3B, the position relation diagram 300B may be detected by using a two-dimensional object detection model trained in advance. For example, when the identifier 310 is detected to be present in the positional relationship diagram 300B, a detection frame may be added to the identifier 310 (e.g., a frame surrounding the identifier 310 in fig. 3B). It should be understood that the rectangular detection frame is merely exemplary, and that the detection frame may also be circular, diamond-shaped, or shaped to just encompass the outer perimeter of the marker, etc., as desired.

Computing device 120 may then determine location information for each identifier based on the determined midpoint of each detection box. It should be understood that the position information of each marker may also be determined based on the lower left corner or other point of each detection box. The computing device 120 may determine whether a target area exists and the location of the target area based on the location information of the identifiers.

The technical solutions described above are only used for illustration and do not limit the invention. It should be understood that the entire monitoring system may also be arranged in other ways and connections. In order to explain the principle of the above scheme more clearly, the process of identifying the target area will be described in more detail below with reference to fig. 4.

Fig. 4 shows a flow diagram of a process 400 for identifying a target area, according to an embodiment of the present disclosure. In some embodiments, the method 400 may be implemented in the computing device 120 shown in FIG. 1. A process 400 for identifying a target area according to an embodiment of the present disclosure is now described with reference to fig. 4. For ease of understanding, specific data mentioned in the following description are exemplary and are not intended to limit the scope of the present disclosure.

At 402, the computing device 120 may determine location information for the identifier 310 detected in the current frame 110 of the video. By way of example, if computing device 120 determines that the number of identifiers 310 in current frame 110 is greater than or equal to a threshold number, it is indicative that these identifiers may be used to surround the target area. Thus, the computing device 120 may determine the location information of the identifiers 310. In this way, the identifier surrounding the target region can be accurately determined in combination with the a priori information. Alternatively or additionally, if computing device 120 determines that the number of identifiers 310 in a predetermined number of consecutive frames in the video (consecutive previous frames 111 up to current frame 110, as shown in fig. 1) is greater than or equal to a threshold number, then it is indicated that these identifiers 310 are stably present in the video for a period of time, and that these identifiers 310 may be used to surround a target region. Thus, the computing device 120 may determine the location information of the identifiers 310. In this way, unstable recognition results can be filtered out.

In some embodiments, the identifier may be a cone-barrel as shown in fig. 2, 3A, and 3B. Alternatively or additionally, the marker may also be a cylindrical barrel, a road-block ball or the like having a road-block function and having a specific shape and color. Such identifiers each have a particular shape that facilitates identification by the computing device 120.

At 404, the computing device 120 may determine a number of categories for classifying the identifier 310. It should be understood that the category number may be an integer value pre-selected based on a priori information, which may be a K value in a K-means algorithm, for example, if the number of target regions in the surveillance video is usually 1 and at most 2, the K value may be selected as 1. As an example, the number of categories K may be determined by conventional elbow rules. For example, after each clustering, the cluster compactness of all points in the class and the cluster center is calculated and is recorded as compact _ K, the descending trend of compact _ K is gradually reduced along with the increase of the number of the cluster centers, and when the descending trend of compact _ K is found to be low or is found to be increased, the number of the cluster centers can be determined as the class number K. For example, the clustering may be performed starting from K ═ 1, and after K > ═ 2, the degree of lift Diff of the difference between the K-th compactness and the K-1-th compactness with respect to the degree of lift between the 1 st compactness and the 2 nd compactness is calculated each time. If the value of Diff at a certain time is less than e.g. 0.4, the current best cluster center is considered to be found.

At 406, the computing device 120 may classify the identifier 310 based on the location information and the number of categories determined above. As an example, the computing device 120 may perform K-means clustering on the identifiers 310 based on the location information and the number of categories. As shown in FIG. 3B, the computing device 120 may determine the identifiers 310-1, 310-2, 310-3, 310-4 as belonging to a primary category and the identifier 310-5 as belonging to another category.

At 408, the computing device 120 may determine an area surrounded by the identifiers 310-1, 310-2, 310-3, 310-4 belonging to the primary category as the target area 130 based on the results of the above classification. It is understood that a "major class" is one or more classifications determined by a classification model, classification function, or algorithm, and that the number of identifiers in the classification is greater than a threshold number, e.g., 3 or 4.

The computing device 120 enables identification of a target area surrounded by the markers by identifying the plurality of markers and clustering the location information thereof. In this way, the computing device 120 may update the road condition information accurately in time, thereby providing reliable data support for path planning of the vehicle.

In some embodiments, there may be a problem of classification errors since the number of classes K is artificially determined based on a priori information. For example, it may be possible to categorize the identifiers 310-1, 310-2, 310-3, 310-4 into a category with the identifier 310-5. To do so, the computing device 120 needs to verify the classification results. By way of example, the computing device 120 may calculate a distance from one of the identifiers 310-1, 310-2, 310-3, 310-4 and 310-5 belonging to the primary category to another of the identifiers 310-1, 310-2, 310-3, 310-4 and 310-5 belonging to the primary category. If there is a distance in the determined distances that is greater than the threshold distance, i.e., there is an identifier 310-5 that is too far from other identifiers, then a classification error is indicated. Thus, the computing device 120 may increase the number of categories, e.g., change them to K + 1. Since there is a classification error, indicating that the target area determined based on the previous classification result is in error, the computing device 120 updates the target area 130 based on the location information of each identifier and the increased number of classes K + 1. In this way, the computing device 120 can detect whether the classification result is correct at any time and correct the incorrect classification result, thereby improving the accuracy of identifying the target area.

It should be understood that there are many ways to verify the classification results. For example, the computing device 120 may traverse each category and calculate whether there is one of the identifiers belonging to the category that is more than a threshold distance from at least two of all other identifiers in the category. If such an identifier is present, it indicates that a classification error is present. Computing device 120 may perform the adjustments described above to update target area 130.

In some embodiments, the computing device 120 may also perform update detection on the target area 130. As an example, the computing device 120 may cluster the detected identifiers in each frame of the video using a K-means algorithm. The results of the classification can then be examined as described above. For example, each classification is traversed to calculate whether there is one of the identifiers belonging to the classification that is more than a threshold distance from at least two of all other identifiers in the classification. If such an identifier is present, it indicates that a classification error is present. Computing device 120 may add 1 to the number of categories and re-cluster. If no suitable classification can be found for a predetermined number of attempts, the target area 130 is lost, and the recognition result is updated.

In addition, the computing device 120 may also compare the determined current contour of the target region 130 to a previous contour of a previous target region determined based on a previous frame 111 in the video. If the ratio of overlap of the current contour and the previous contour is less than or equal to a threshold ratio (e.g., 80%), the previous target region may be updated with the target region 130, otherwise, the target region 130 is unchanged. Specifically, the computing device 120 may traverse all previous contours already existing, find the previous contour that most coincides with the current contour, and note the repetition rate between the two. In this manner, the computing device 120 may determine whether the newly determined target area has ever changed and report the change.

It should be understood that the target area identification method of the present disclosure has an advantage over the conventional identification method in that the target area identification method of the present disclosure does not need to acquire and report information by manual means as in the conventional method whenever a new target area appears in a monitored video. The reason is that the present disclosure utilizes, for example, a K-means clustering means to cluster the plurality of identified markers, and then finds a class suitable for forming the target region, thereby completing the identification of the target region. Therefore, the road provided with the monitoring camera equipment can be fully covered on the premise of not carrying out too much manual intervention, timely and accurate road information is provided for a user, the human resource cost is saved, and the user experience is improved.

Fig. 5 shows a block diagram of an apparatus 500 for identifying a target area 130 according to an embodiment of the present disclosure. As shown in fig. 5, the apparatus 500 may include: a location information determining module 502 configured to determine location information of the marker detected in the current frame of the video; a number of categories determination module 504 configured to determine a number of categories for classifying the identifier; a classification module 506 configured to classify the identifier based on the location information and the number of categories; and a target area determination module 508 configured to determine, as the target area, an area surrounded by the identifiers belonging to the primary category, the number of the identifiers belonging to the primary category being greater than a first threshold number, based on a result of the classification.

In some embodiments, the apparatus 500 may further comprise: a distance calculation module configured to calculate a distance from one of the identifiers belonging to the primary category to another of the identifiers belonging to the primary category; a number of categories increasing module configured to increase the number of categories if it is determined that there is a distance greater than a threshold distance in the distances; and a target area update module configured to update the target area based on the location information and the increased number of categories.

In some embodiments, the location information determination module 502 may be configured to: determining the location information if the number of identifiers in a predetermined number of consecutive frames in the video is determined to be greater than or equal to the second threshold number.

In some embodiments, the location information determination module 502 may be configured to: determining the location information if the number of identifiers in the current frame is determined to be greater than or equal to a third threshold number.

In some embodiments, the apparatus 500 may further comprise: a contour comparison module configured to compare the determined current contour of the target region to a previous contour of a previous target region determined based on a previous frame in the video; and an update module configured to update a previous target region with a target region in response to a ratio of overlap of the current contour and the previous contour being less than or equal to a threshold ratio.

In some embodiments, the classification module 506 may include: a K-means clustering module configured to perform K-means clustering on the identifier based on the location information and the number of categories.

In some embodiments, the identifier may include at least one of a cone, a cylinder, and a barricade ball.

FIG. 6 illustrates a block diagram of a computing device 600 capable of implementing multiple embodiments of the present disclosure. Device 600 may be used to implement computing device 120 of fig. 1. As shown, device 600 includes a Central Processing Unit (CPU)601 that may perform various appropriate actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM)602 or loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The CPU601, ROM602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 504.

A number of components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, or the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The processing unit 601 performs the various methods and processes described above, such as the

processes

200, 300, and 400. For example, in some embodiments, processes 200, 300, and 400 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM602 and/or the communication unit 609. When the computer program is loaded into RAM 603 and executed by CPU601, one or more of the steps of

processes

200, 300, and 400 described above may be performed. Alternatively, in other embodiments, CPU601 may be configured to perform

processes

200, 300, and 400 by any other suitable means (e.g., by way of firmware).

The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a load programmable logic device (CPLD), and the like.

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.

Further, while operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

1. A method for identifying a target area, comprising:

determining location information of the marker detected in a current frame of the video;

determining a number of categories for classifying the identifier;

classifying the identifier based on the location information and the number of categories; and

based on the result of the classification, a region surrounded by the markers belonging to the main category whose number is larger than a first threshold number is determined as the target region.

2. The method of claim 1, further comprising:

calculating a distance from one of the identifiers belonging to the primary category to another of the identifiers belonging to the primary category;

increasing the number of categories if it is determined that there is a distance greater than a threshold distance among the distances; and

reclassifying the identifier based on the location information and the increased number of categories; and

updating the target area based on a result of the reclassification.

3. The method of claim 1, wherein determining the location information for the identifier comprises:

determining the location information if the number of identifiers in a predetermined number of consecutive frames in the video is determined to be greater than or equal to the second threshold number.

4. The method of claim 1, wherein determining the location information for the identifier comprises:

determining the location information if the number of identifiers in the current frame is determined to be greater than or equal to a third threshold number.

5. The method of claim 1, further comprising:

comparing the determined current contour of the target region to a previous contour of a previous target region determined based on a previous frame in the video; and

updating the previous target region with the target region in response to a ratio of overlap of the current contour and the previous contour being less than or equal to a threshold ratio.

6. The method of claim 1, wherein classifying the identifier comprises:

performing K-means clustering on the identifiers based on the location information and the number of categories.

7. The method of claim 1, wherein the identifier comprises at least one of:

a conical barrel;

a cylindrical barrel; and

a road block ball.

8. An apparatus for identifying a target area, comprising:

a location information determination module configured to determine location information of the marker detected in a current frame of the video;

a number of categories determination module configured to determine a number of categories for classifying the identifier;

a classification module configured to classify the identifier based on the location information and the number of categories; and

a target area determination module configured to determine, as the target area, an area surrounded by the identifiers belonging to a primary category, the number of the identifiers belonging to the primary category being greater than a first threshold number, based on a result of the classification.

9. The apparatus of claim 8, further comprising:

a distance calculation module configured to calculate a distance from one of the identifiers belonging to the primary category to another of the identifiers belonging to the primary category;

a number of categories increasing module configured to increase the number of categories if it is determined that there is a distance greater than a threshold distance in the distances; and

a target area update module configured to update the target area based on the location information and the increased number of categories.

10. The apparatus of claim 8, wherein the location information determination module is configured to:

11. The apparatus of claim 8, wherein the location information determination module is configured to:

12. The apparatus of claim 8, further comprising:

a contour comparison module configured to compare the determined current contour of the target region to a previous contour of a previous target region determined based on a previous frame in the video; and

an update module configured to update the previous target region with the target region in response to a ratio of overlap of the current contour and the previous contour being less than or equal to a threshold ratio.

13. The apparatus of claim 8, wherein the classification module comprises:

a K-means clustering module configured to perform K-means clustering on the identifier based on the location information and the number of categories.

14. The apparatus of claim 8, wherein the identifier comprises at least one of:

a conical barrel;

a cylindrical barrel; and

a road block ball.

15. An electronic device, the electronic device comprising:

one or more processors; and

storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out the method according to any one of claims 1-7.

16. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.

17. A roadside apparatus comprising the electronic apparatus of claim 15.