Mobile object detection method and apparatus, electronic device, and storage medium

Abstract

The application provides a mobile object detection method, which comprises the following steps: acquiring point cloud data set and a first image of a target scene; identifying a stationary object region in the first image; recording point cloud data corresponding to the stationary object region in the point cloud data set as first point cloud data; obtaining a first speed range according to the first point cloud data; and judging whether a mobile object exists in the target scene according to second point cloud data and the first speed range, wherein the second point cloud data is point cloud data in the point cloud data set except the first point cloud data. The application also discloses a mobile object detection device, a computer storage medium and an electronic device. The point cloud data obtained by combining a radar can improve the accuracy of detecting a mobile object.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of moving object detection, and in particular to a moving object detection method and device, an electronic device, and a storage medium. BACKGROUND

[0002] Automated driving (ADS) technology is a process of perceiving information of surrounding environment through a sensing system, making a decision and planning of a driving path based on the perceived information, and controlling itself to complete driving automatically based on the planned driving path. For example, the current automated driving technology can be used for a smart / intelligent car, which perceives a road environment by a vehicle-mounted sensing system of the car, then makes a driving path planning by a vehicle control system, and controls itself to realize automated driving based on the planned driving path.

[0003] In the above process of automated driving, a moving object on a driving section is recognized through an image, and then a future trajectory of the recognized moving object is predicted to make an obstacle avoidance path planning. However, it is impossible to know which object is a moving object only by the image, such as recognizing that the object in the image is a vehicle, but it is impossible to determine whether the vehicle is moving. SUMMARY

[0004] Therefore, it is necessary to provide a moving object detection method and device, an electronic device, and a storage medium, which can improve the accuracy of detecting moving objects by combining point cloud data obtained by a radar.

[0005] In a first aspect, the present application provides a moving object detection method applied to an electronic device, which includes: obtaining a point cloud data set and a first image of a target scene, recognizing a static object region in the first image, recording point cloud data corresponding to the static object region in the point cloud data set as first point cloud data, obtaining a first speed range according to the first point cloud data, and judging whether there is a moving object in the target scene according to second point cloud data and the first speed range, wherein the second point cloud data is point cloud data in the point cloud data set except the first point cloud data.

[0006] In some embodiments, the recording of the point cloud data corresponding to the static object region in the point cloud data set as the first point cloud data includes:

[0007] Converting the point cloud data set into a two-dimensional coordinate to obtain a two-dimensional coordinate set;

[0008] For each two-dimensional coordinate in the two-dimensional coordinate set, judging whether the two-dimensional coordinate corresponds to the static object region;

[0009] If yes, record the point cloud data corresponding to the two-dimensional coordinates as first point cloud data.

[0010] In some embodiments, the determining whether the two-dimensional coordinates correspond to the stationary object region comprises:

[0011] obtaining a coordinate range of the stationary object region, wherein the stationary object region is a region where the ground is located;

[0012] determining whether the two-dimensional coordinates are located in the coordinate range;

[0013] If yes, the two-dimensional coordinates correspond to the stationary object region.

[0014] In some embodiments, the obtaining a first speed range according to the first point cloud data comprises:

[0015] obtaining a first speed of each of the first point cloud data;

[0016] obtaining the first speed range according to the first speed.

[0017] In some embodiments, the determining whether there is a moving object in the target scene according to the second point cloud data and the first speed range comprises:

[0018] determining whether a speed of the second point cloud data is in the first speed range;

[0019] If no, recording a first object corresponding to the second point cloud data as the moving object, wherein the first object is an object in the target scene.

[0020] In some embodiments, the method further comprises:

[0021] detecting that the speed of the second point cloud data is not in the first speed range, and determining a first pixel of the first image according to two-dimensional coordinates corresponding to the second point cloud data;

[0022] determining a region where the first pixel is located as a moving object region.

[0023] In some embodiments, the electronic device comprises a radar and a camera device.

[0024] The obtaining a point cloud data set and a first image of a target scene comprises:

[0025] obtaining the point cloud data set of the target scene by the radar;

[0026] obtaining a first image of the target scene by the camera device, wherein the first image and the point cloud data set are obtained at the same time.

[0027] The second aspect provides a moving object detection device, which comprises:

[0028] an acquisition module configured to acquire a point cloud data set and a first image of a target scene;

[0029] an identification module configured to identify a static object region in the first image;

[0030] a recording module configured to record point cloud data corresponding to the static object region in the point cloud data set as first point cloud data;

[0031] a derivation module configured to derive a first speed range according to the first point cloud data;

[0032] a judgment module configured to judge whether a moving object exists in the target scene according to second point cloud data and the first speed range, wherein the second point cloud data is point cloud data in the point cloud data set except the first point cloud data.

[0033] The third aspect provides a computer storage medium, which stores a plurality of instructions, and the plurality of instructions are adapted to be loaded and executed by a processor to implement the moving object detection method.

[0034] The fourth aspect provides an electronic device, which comprises:

[0035] a processor configured to implement one or more instructions; and

[0036] a computer storage medium configured to store a plurality of instructions, and the plurality of instructions are adapted to be loaded and executed by the processor to implement the moving object detection method.

[0037] The embodiments of the present application have at least the following beneficial effects:

[0038] A new mobile object detection scheme is proposed. By obtaining the point cloud data set and the first image of the target scene at the same time, the stationary object region in the first image is identified through image processing. The point cloud data corresponding to the stationary object region in the point cloud data set is found, that is, the point cloud data corresponding to the stationary object in the point cloud data set is found, and the point cloud data is recorded as the first point cloud data. The first speed range is determined according to the speed of the first point cloud data, so that the first speed range of the stationary object can be obtained. Further, the relationship between the speed of the second point cloud data and the first speed range is judged, that is, the relative relationship between the object corresponding to the second point cloud data and the stationary object is judged. If the speed of the second point cloud data is within the first speed range, the object corresponding to the second point cloud data is stationary relative to the stationary object, and if the speed of the second point cloud data is not within the first speed range, the object corresponding to the second point cloud data is mobile relative to the stationary object. If the stationary object is selected as the ground, the object moving relative to the ground can be determined as the mobile object. In combination with the point cloud data obtained by the radar, the accuracy of detecting the mobile object can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 An electronic device structure schematic diagram provided by an embodiment of the present application.

[0040] Figure 2 A mobile object detection device structure schematic diagram provided by an embodiment of the present application.

[0041] Figure 3 A mobile object detection method flowchart provided by an embodiment of the present application.

[0042] Figure 4 A first point cloud data recording method flowchart provided by an embodiment of the present application.

[0043] Figure 5 A stationary object region corresponding judgment method flowchart provided by an embodiment of the present application.

[0044] Figure 6 A mobile object existence judgment method flowchart provided by an embodiment of the present application.

[0045] Main element symbol explanation

[0046] Electronic device 100

[0047] Mobile object detection device 200

[0048] Memory 10

[0049] Processor 20

[0050] Radar 30

[0051] Camera 40

[0052] Communication unit 50

[0053] Input / output unit 60

[0054] Acquisition module 210

[0055] Identification module 220

[0056] Recording module 230

[0057] Obtaining module 240

[0058] Judgment module 250

[0059] The following detailed description will further describe the present application in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0061] It should be noted that when one component is considered to be "connected" to another component, it can be directly connected to the other component or can exist with a component disposed therebetween. When one component is considered to be "disposed on" another component, it can be directly disposed on the other component or can exist with a component disposed therebetween.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0063] Referring to Figure 1 The embodiments of the present application provide an electronic device 100. The electronic device 100 includes a memory 10, a processor 20, and a moving object detection apparatus 200 stored in the memory 10 and executable on the processor 20. The processor 20 can be used to execute the steps in the moving object detection method embodiments, for example Figures 3 to 6The processor 20 implements the steps in the embodiment of the moving object detection method when executing the moving object detection apparatus 200, or the processor 20 implements the functions of the modules in the moving object detection apparatus 200 when executing the moving object detection apparatus 200. For example, the processor 20 implements the functions of the modules 210-250 in the moving object detection apparatus 200 when executing the moving object detection apparatus 200. Figure 2

[0064] The moving object detection apparatus 200 can be divided into one or more modules, which are stored in the memory 10 and executed by the processor 20 to complete the embodiment of the moving object detection method. One or more modules can be a series of computer program instructions capable of completing a specific function, which are used to describe the execution process of the moving object detection apparatus 200 in the electronic device 100. For example, the moving object detection apparatus 200 can be divided into the acquisition module 210, the identification module 220, the recording module 230, the obtaining module 240, and the judgment module 250 in the embodiment of the moving object detection method. The specific functions of the above modules are described below. Figure 2

[0065] The processor 20 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor 20 can be any conventional processor 20, etc. The processor 20 can be connected to various parts of the electronic device 100 through various interfaces and buses.

[0066] The memory 10 can be used to store the moving object detection apparatus 200 and / or the modules. The processor 20 realizes various functions of the electronic device 100 by running or executing the computer programs and / or modules stored in the memory 10, and calling the data stored in the memory 10. The memory 10 can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory card, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device.

[0067] ​​In an embodiment, the electronic device 100 further comprises a radar 30, which is an electronic device 100 that uses electromagnetic waves to detect objects in a target scene. The radar 30 scans the target scene to obtain a point cloud dataset of the target scene, which comprises a plurality of point cloud data, wherein the point cloud data comprises information such as the distance from the object in the target scene to the point of emission of the electromagnetic wave, the rate of change of the distance (radial velocity), the azimuth, the altitude, the speed, and the direction of movement. The radar 30 is connected to the processor 20, and the radar 30 transmits the obtained point cloud dataset to the processor 20. The point cloud data is also referred to as point cloud data (PCD) data or three-dimensional point cloud data, which can be a set of a plurality of points that represent the spatial distribution of an object and the surface characteristics of the object by obtaining the three-dimensional spatial coordinates of each sampling point on the surface of the object in the target scene using a laser in the same spatial reference system. Compared with an image, the point cloud data lacks detailed texture information, but contains rich three-dimensional spatial information, the distance between the object and the radar 30, and the speed of the object.

[0068] More specifically, the radar 30 comprises a transmitter and a receiver, and the receiver is usually located at the same position as the transmitter on the electronic device 100. The transmitter transmits pulses of radio frequency waves, and the pulses of radio frequency waves bounce off any objects in their path. The pulses reflected by the objects return a small portion of the energy of the radio frequency waves to the receiver. The receiver can receive the radio frequency waves reflected by a plurality of objects in the target scene, that is, a plurality of point cloud data about the target scene can be obtained, and the radar 30 obtains a point cloud dataset of the target scene.

[0069] The radar 30 can be a vehicle-mounted millimeter wave radar, a laser radar, an ultrasonic radar, or the like.

[0070] In an embodiment, the electronic device 100 further comprises a camera 40, which can be used to obtain a first image of the target scene. The camera 40 is connected to the processor 20 and transmits the obtained first image to the processor 20. The camera 40 is also referred to as a camera or a lens. If the electronic device 100 is implemented as a vehicle, the camera 40 can be implemented as a drive recorder.

[0071] In an embodiment, the electronic device 100 further comprises a communication unit 50, which is used to establish a communication connection with other computer devices through wired or wireless means. The communication unit 50 can be a wired communication unit or a wireless communication unit.

[0072] In an embodiment, the electronic device 100 further comprises an input / output unit 60, which comprises a keyboard, a mouse, a display screen, and the like, and the display screen is used to display the media files of the electronic device 100.

[0073] The electronic device 100 can be a vehicle-mounted device, a vehicle (e.g., an autonomous vehicle), a mobile robot (e.g., a cleaning robot, a tour guide robot), a cloud server, and the like. Those skilled in the art can understand that the schematic diagram is only an example of the electronic device 100, and does not constitute a limitation on the electronic device 100, and can include more or fewer components than the diagram, or combine certain components, or different components, for example, the electronic device 100 can also include a network access device, a bus, and the like.

[0074] Referring to Figure 2 The embodiments of the present application provide a moving object detection apparatus 200. The moving object detection apparatus 200 can include an obtaining module 210, an identifying module 220, a recording module 230, an obtaining module 240, and a judging module 250.

[0075] In an embodiment, the above modules can be programmable software instructions stored in the memory 10 and executable by the processor 20. It can be understood that in other embodiments, the above modules can also be program instructions or firmware fixed in the processor 20.

[0076] The obtaining module 210 is configured to obtain a point cloud data set of a target scene and a first image.

[0077] The identifying module 220 is configured to identify a static object region in the first image.

[0078] The recording module 230 is configured to record point cloud data corresponding to the static object region in the point cloud data set as first point cloud data.

[0079] The obtaining module 240 is configured to obtain a first speed range according to the first point cloud data.

[0080] The judging module 250 is configured to judge whether there is a moving object in the target scene according to the second point cloud data and the first speed range, wherein the second point cloud data is point cloud data in the point cloud data set except the first point cloud data.

[0081] Referring to Figure 3 The embodiments of the present application provide a flow of a moving object detection method. The moving object detection method can be applied to the above Figure 1 The electronic device 100 shown in the above figure can be executed by the processor 20. According to different needs, the order of steps in the flowchart can be changed, and some steps can be omitted. The following will be specifically explained.

[0082] Step S31: obtaining a point cloud data set of a target scene and a first image.

[0083] In the embodiment of the present application, step S31 can specifically include that the electronic device 100 obtains a point cloud data set of the target scene through the radar 30, and obtains a first image of the target scene through the camera 40. The processor 20 receives the point cloud data set transmitted by the radar 30 and the first image transmitted by the camera 40.

[0084] The first image and the point cloud data set obtained by the processor 20 in step S31 are respectively the data obtained by the radar 30 and the camera 40 at the same time, that is, the time when the radar 30 obtains the point cloud data set is the same as the time when the camera 40 obtains the first image.

[0085] Exemplarily, the electronic device 100 is a vehicle, and the radar 30 and the camera 40 are located in front of the vehicle. During the driving of the vehicle, the camera 202 collects the image of the target scene in front of the vehicle, obtains the first image, records the time when the first image is obtained, and obtains the time stamp of the first image. The radar 30 scans the target scene in front of the vehicle, obtains the point cloud data, records the time when the point cloud data is obtained, and obtains the time stamp of the point cloud data. The point cloud data set and the time stamp of the point cloud data set are obtained by obtaining the point cloud data with the same time stamp. After the processor 20 receives the point cloud data set and the first image, the first image and the point cloud data set with the same time stamp are selected according to the time stamp. The first image and the point cloud data set with the same time stamp are used for subsequent steps S32-S35.

[0086] Step S32: Identify the static object region in the first image.

[0087] In the embodiment of the present application, the classification network can be stored in the processor 20, and the classification network can be a pre-trained neural network. The classification network can be used to identify an image, for example, to identify a person, a vehicle, a traffic light, a building, a road, a ground, a separation island, etc. in the image. The classification network is a mature existing technology, and will not be described here.

[0088] In the embodiment of the present application, the static object region is the region where the static object in the first image is located, and the static object can be a traffic light, a building, a road, a ground, etc.

[0089] In the embodiment of the present application, the static object to be used as a reference can be determined in advance, for example, based on the application of the electronic device 100 to a vehicle, the ground or the road can be selected as the static object. The region where the static object to be used as a reference is identified in the first image is the static object region. For example, the static object is the ground, and the static object region is the region where the ground is located.

[0090] Step S33: Record the point cloud data corresponding to the static object region in the point cloud data set as first point cloud data.

[0091] In some embodiments, referring to Figure 4 , step S33 can specifically be:

[0092] Step S41: converting the point cloud data set into two-dimensional coordinates to obtain a two-dimensional coordinate set.

[0093] In the embodiments of the present application, the processor 20 can pre-acquire the intrinsic parameters of the camera 40 and the extrinsic parameters between the camera 40 and the radar 30, and then convert the point cloud data set into a two-dimensional coordinate set according to the extrinsic parameters and the intrinsic parameters. It can be understood that acquiring the intrinsic parameters of the camera 40 and the extrinsic parameters between the camera 40 and the radar 30 is a mature existing technology, and converting a three-dimensional point cloud image into a two-dimensional image according to the extrinsic parameters is a mature existing technology, which will not be described here.

[0094] Exemplarily, the processor 20 converts the point cloud data into two-dimensional coordinates according to the following conversion formula, and for each point cloud data, a two-dimensional coordinate set can be obtained. The conversion formula is as follows:

[0095]

[0096] wherein x w , y w , z w are three-dimensional coordinates of the radar 30 point cloud data, K is the intrinsic parameter of the camera 40, is the extrinsic parameter between the radar 30 and the camera 40, and u and v are the converted two-dimensional coordinates.

[0097] Step S42: judging whether each two-dimensional coordinate in the two-dimensional coordinate set corresponds to a stationary object region.

[0098] In the embodiments of the present application, the stationary object region in the first image corresponds to a stationary object in the target scene, for example, the stationary object region is a ground region, and the stationary object region corresponds to the ground in the target scene. The radar 30 can scan each object or different positions of the same object in the target scene, for example, a point A in the target scene corresponding to a two-dimensional coordinate is a point a of an A object in the target scene. A point B in the target scene corresponding to another two-dimensional coordinate is a point b of a B object in the target scene. Therefore, for each two-dimensional coordinate in the two-dimensional coordinate set, it is judged whether the two-dimensional coordinate corresponds to a stationary object region. In other words, it is judged whether the object in the target scene corresponding to the two-dimensional coordinate is a stationary object indicated by the stationary object region. Or, it is judged whether the spatial point in the target scene corresponding to the two-dimensional coordinate is within the spatial region range of the stationary object in the target scene.

[0099] In some embodiments, referring to Figure 5 , step S42 can specifically be:

[0100] Step S51: Obtain the coordinate range of the stationary object region.

[0101] Step S52: Determine whether the two-dimensional coordinate is located in the coordinate range.

[0102] If yes, step S53: The two-dimensional coordinate corresponds to the stationary object region.

[0103] If no, step S54: The two-dimensional coordinate does not correspond to the stationary object region.

[0104] It should be noted that the two-dimensional coordinate point on the two-dimensional coordinate set can correspond to the pixel point on the first image. If the radar 30 scans the point a on the A object in the target scene, obtains the point cloud data of the point a, and converts the point cloud data into a two-dimensional coordinate (x, y). Correspondingly, the pixel coordinate (x1, y1) corresponding to the point a on the A object in the target scene can be found in the first image. The two-dimensional coordinate (x, y) and the pixel coordinate (x1, y1) on the first image can be the same or different. If they are different, the relationship between the two-dimensional coordinate and the first image pixel point can be determined according to the intrinsic parameters of the camera device 40 and the extrinsic parameters between the camera device 40 and the radar 30.

[0105] Exemplarily, taking the coordinate range of the stationary object region as ((0, 0), (3, 0), (3, 3), (0, 3)) as an example, if the two-dimensional coordinate (x, y) is the same as the pixel coordinate (x1, y1) on the first image, that is, corresponding to the point a on the A object in the target scene, the two-dimensional coordinate obtained after the point cloud data of the point a is converted is (3, 0), and the pixel coordinate of the point a on the first image is also (3, 0). It can be determined that the two-dimensional coordinate (3, 0) of the point cloud data is located in the coordinate range of the stationary object region ((0, 0), (3, 0), (3, 3), (0, 3)), and the two-dimensional coordinate corresponds to the stationary object region. If the two-dimensional coordinate (x, y) is not the same as the pixel coordinate (x1, y1) on the first image, the relationship between the two-dimensional coordinate and the first image pixel point can be determined according to the relationship between the two-dimensional coordinate and the first image pixel point, and the two-dimensional coordinate is determined to fall into the coordinate range.

[0106] If yes, step S43: Record the point cloud data corresponding to the two-dimensional coordinate as the first point cloud data.

[0107] In the embodiment of the present application, by comparing the point cloud data of the radar 30 with the first image, the point cloud data corresponding to the stationary object region is screened out, that is, the point cloud data scanned to the stationary object is screened out, and recorded as the first point cloud data.

[0108] If no, step S44: do not record the point cloud data corresponding to the two-dimensional coordinate as the first point cloud data. In some embodiments, if the two-dimensional coordinate is not located in the coordinate range, the point cloud data corresponding to the two-dimensional coordinate can be recorded as the second point cloud data.

[0109] Step S34: obtain the first speed range according to the first point cloud data.

[0110] In the embodiments of the present application, the radar 30 scans the object in the target scene, and the speed of the object can be obtained according to the point cloud data of the scanned object. That is, when the receiver receives the point cloud data of the object in the target scene reflecting the radio frequency wave, the speed information of the object reflecting the radio frequency wave is carried in the point cloud data.

[0111] In the embodiments of the present application, the first point cloud data can include one or more. For example, the radio frequency wave emitted by the transmitter scans 100 points on the ground, and the receiver receives the radio frequency wave reflected back by the 100 points. The 100 point cloud data is the first point cloud data. The minimum value of the speed of the 100 point cloud data is 5.5 km / h, and the maximum value of the speed is 5.7 km / h, so the first speed range is 5.5 km / h to 5.7 km / h.

[0112] Step S35: determine whether there is a moving object in the target scene according to the second point cloud data and the first speed range, wherein the second point cloud data is the point cloud data in the point cloud data set except the first point cloud data.

[0113] In some embodiments, referring to Figure 6 , step S35 can specifically include:

[0114] Step S61: determine whether the speed of the second point cloud data is in the first speed range.

[0115] If no, step S62: record the first object corresponding to the second point cloud data as a moving object. The first object is an object in the target scene.

[0116] If yes, step S63: record the first object corresponding to the second point cloud data as a stationary object.

[0117] In the embodiments of the present application, if the speed of the second point cloud data is in the first speed range, the first object of the target scene corresponding to the point cloud data is stationary relative to the stationary object. If the speed of the second point cloud data is not in the first speed range, the first object of the target scene corresponding to the point cloud data is moving relative to the stationary object. Based on the stationary object being the ground, if the first object is stationary relative to the ground, the first object is a stationary object. If the first object moves relative to the ground, the first object is a moving object.

[0118] In some embodiments, the method further comprises: detecting that the speed of the second point cloud data is not within the first speed range, and determining a first pixel of the first image according to the two-dimensional coordinates corresponding to the second point cloud data. An area in which the first pixel is located is determined as the moving object area.

[0119] Specifically, according to the speed carried by the point cloud data, it is determined whether the object scanned by the radar 30 is a moving object. That is, it is determined whether the speed of the second point cloud data is within the first speed range. If not, the object of the target scene corresponding to the point cloud data is a moving object. According to the two-dimensional coordinates corresponding to the point cloud data, the moving object area is marked on the first image.

[0120] In the embodiments of the present application, the point cloud data set of the target scene at the same time and the first image are obtained, and the static object area in which the static object in the first image is located is identified through image processing. The point cloud data corresponding to the static object area in the point cloud data set is found, that is, the point cloud data corresponding to the static object in the point cloud data set is found, and the point cloud data is recorded as the first point cloud data. The first speed range is determined according to the speed of the first point cloud data, so that the first speed range of the static object can be obtained. Further, the relationship between the speed of the second point cloud data other than the first point cloud data and the first speed range is determined, that is, the relative relationship between the object corresponding to the second point cloud data and the static object is determined. If the speed of the second point cloud data is within the first speed range, the object corresponding to the second point cloud data is stationary relative to the static object, and if the speed of the second point cloud data is not within the first speed range, the object corresponding to the second point cloud data moves relative to the static object. If the static object is selected as the ground, it can be determined that the object moving relative to the ground is a moving object.

[0121] The embodiments of the present application also provide a computer readable storage medium, which stores at least one instruction, and the at least one instruction is executed by the processor 20 to realize the moving object detection method as described above.

[0122] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be regarded as exemplary and non-limiting, and the scope of the present application is defined by the appended claims rather than the above description, and all changes falling within the meaning and range of the equivalent elements of the claims are intended to be included in the present application. Any reference signs in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "comprise" does not exclude other units or, the singular does not exclude the plural. The plurality of units or devices stated in the device claims can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any specific order.

[0123] Finally, it should be noted that the above examples are merely intended to illustrate the technical solutions of the present application and not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced, without departing from the spirit and scope of the technical solutions of the present application.

[0124] Those skilled in the technical field should recognize that the above embodiments are merely intended to illustrate the present application and not to limit the present application. As long as the above embodiments are within the spirit and scope of the present application, any appropriate changes and variations made to the above embodiments fall within the scope of the present application.

Claims

1. A moving object detection method characterized by comprising: The method is applied to an electronic device, and comprises: obtaining a point cloud data set and a first image of a target scene; identifying a static object region in the first image; recording point cloud data corresponding to the static object region in the point cloud data set as first point cloud data; obtaining a first speed range according to the first point cloud data; judging whether a moving object exists in the target scene according to second point cloud data and the first speed range, wherein the second point cloud data is point cloud data in the point cloud data set except the first point cloud data.

2. The method of claim 1, wherein, The recording of the point cloud data corresponding to the static object region in the point cloud data set as the first point cloud data comprises: converting the point cloud data set into a two-dimensional coordinate set; judging whether each two-dimensional coordinate in the two-dimensional coordinate set corresponds to the static object region; if yes, recording point cloud data corresponding to the two-dimensional coordinate as the first point cloud data.

3. The method of claim 2, wherein, The judgment of whether the two-dimensional coordinate corresponds to the static object region comprises: obtaining a coordinate range of the static object region, wherein the static object region is a region where the ground is located; judging whether the two-dimensional coordinate is located in the coordinate range; if yes, the two-dimensional coordinate corresponds to the static object region.

4. The method of claim 3, wherein, The obtaining of the first speed range according to the first point cloud data comprises: obtaining a first speed of each first point cloud data; obtaining the first speed range according to the first speed.

5. The method according to any one of claims 1 to 4, characterized in that, The judgment of whether a moving object exists in the target scene according to the second point cloud data and the first speed range comprises: judging whether a speed of the second point cloud data is in the first speed range; if no, recording a first object corresponding to the second point cloud data as the moving object, wherein the first object is an object in the target scene.

6. The method of claim 5, wherein, The method further comprises: detecting that the speed of the second point cloud data is not in the first speed range, determining a first pixel of the first image according to a two-dimensional coordinate corresponding to the second point cloud data; determining a region where the first pixel is located as a moving object region.

7. The method of claim 1, wherein, The electronic device comprises a radar and a camera device; The obtaining of the point cloud data set and the first image of the target scene comprises: obtaining the point cloud data set of the target scene through the radar; obtaining a first image of the target scene through the camera device, wherein the first image and the point cloud data set are obtained at the same time.

8. A moving object detection apparatus characterized by comprising: The device comprises: an obtaining module, configured to obtain a point cloud data set and a first image of a target scene; an identifying module, configured to identify a static object region in the first image; a recording module, configured to record point cloud data corresponding to the static object region in the point cloud data set as first point cloud data; an obtaining module, configured to obtain a first speed range according to the first point cloud data; a judging module, configured to judge whether a moving object exists in the target scene according to second point cloud data and the first speed range, wherein the second point cloud data is point cloud data in the point cloud data set except the first point cloud data.

9. A computer storage medium, characterized in that: The computer storage medium stores a plurality of instructions adapted to be loaded by the processor and to execute the method as claimed in any one of claims 1 to 7.

10. An electronic device, comprising: Comprise: a processor to implement one or more instructions; and a computer storage medium to store a plurality of instructions adapted to be loaded by the processor and to execute the method as claimed in any one of claims 1 to 7.

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