CN112034464A - Target classification method - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
- G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
- G01S13/52—Discriminating between fixed and moving objects or between objects moving at different speeds
- G01S13/56—Discriminating between fixed and moving objects or between objects moving at different speeds for presence detection
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
- G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
- G01S2013/9321—Velocity regulation, e.g. cruise control
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Abstract
The embodiment of the invention discloses a target classification method, which comprises the following steps: acquiring a relative radar scattering cross section of a target to be classified and a target distance power spectrum entropy value; and inputting the relative radar scattering cross-section area and the target distance spectral line entropy value into a pre-trained target classifier, and determining the class of the target to be classified according to the output result of the target classifier. The method and the device have the advantages that the type of the target is estimated based on two-dimensional characteristics of the scattering cross section area and the entropy value of the relative radar of different targets to be classified, the accuracy of target classification is improved, and the scattering cross section area and the entropy value of the target can be directly calculated based on signals received by the radar and a preset formula, so that the calculated amount is reduced, and the calculation complexity of target classification is further reduced.
Description
Technical Field
The embodiment of the invention relates to the technical field of radars, in particular to a target classification method.
Background
At present, driving auxiliary systems such as a vehicle-mounted self-adaptive cruise control system and the like mostly adopt millimeter wave radars to detect front vehicles and obstacles, wherein target classification is used as a precondition basis for identifying the front vehicles and the obstacles, and the accuracy of the target classification is directly related to the effective identification of the targets by the driving auxiliary systems.
Currently, radar target classification is commonly used, after a target is detected from a radar echo, a characteristic attribute capable of reflecting different targets, such as a speed characteristic, is extracted from an echo signal, and then the target is classified based on the characteristic attribute. However, this approach still has certain disadvantages: the accuracy of target classification is low, and the calculation complexity is high.
Disclosure of Invention
The embodiment of the invention provides a target classification method, which aims to reduce the complexity of target classification calculation and improve the accuracy of target classification.
The embodiment of the invention specifically discloses the following contents:
in a first aspect, an embodiment of the present invention provides a target classification method, where the method includes:
acquiring a relative radar scattering cross section of a target to be classified and a target distance power spectrum entropy value;
and inputting the relative radar scattering cross-section area and the target distance spectral line entropy value into a pre-trained target classifier, and determining the class of the target to be classified according to the output result of the target classifier.
In a second aspect, an embodiment of the present invention further provides an apparatus for classifying an object, where the apparatus includes:
the calculation module is used for acquiring a relative radar scattering cross section of the target to be classified and a power spectrum entropy value of a target distance;
and the classification module is used for inputting the relative radar scattering cross-sectional area and the target distance spectral line entropy value into a pre-trained target classifier and determining the class of the target to be classified according to the output result of the target classifier.
In a third aspect, an embodiment of the present invention further provides an electronic device, including:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a method of object classification as described in any embodiment of the invention.
In a fourth aspect, an embodiment of the present invention further provides a storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the object classification method according to any embodiment of the present invention.
In the embodiment of the invention, the relative radar cross-section area and the target distance power spectrum entropy of the target to be classified are calculated, the relative radar cross-section area and the target distance spectrum entropy are input into a pre-trained target classifier, and the class of the target to be classified is determined according to the output result. Therefore, two-dimensional characteristics of the target relative radar cross-sectional area and the entropy value based on different classifications are extracted, the target classification is estimated, the accuracy of the target classification can be improved, and the target relative radar cross-sectional area and the entropy value can be directly calculated based on signals received by the radar and a preset formula, so that the calculated amount is reduced, and the calculation complexity of the target classification is further reduced.
Drawings
FIG. 1 is a flowchart illustrating a target classification method according to a first embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a target classification apparatus according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device in a third embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a target classification method according to an embodiment of the present invention, where the present embodiment is applicable to a situation where an on-vehicle adaptive cruise control system detects a vehicle ahead and an obstacle, and the method may be executed by a target classification device, which may be implemented in software and/or hardware, and may be integrated on an electronic device, such as a radar device.
As shown in fig. 1, the target classification method specifically includes the following steps:
s101, acquiring a relative radar scattering cross section of a target to be classified and a target distance power spectrum entropy value.
In the embodiment of the invention, the target to be classified refers to the obstacle which needs to be identified and belongs to the category, wherein the obstacle can be a static object or a moving object, the static object is exemplified by a cement pier, a sweeper in a parking state and the like, and the moving object is exemplified by a pedestrian, various vehicles in a driving state and the like.
The Radar Cross Section (RCS) is a physical quantity that characterizes the backscattering capability of a target to incident electromagnetic waves, and the Radar detection principle is that the emitted electromagnetic waves are reflected back to a receiving antenna of the Radar after being irradiated to the surface of an object, and if the more the electromagnetic waves irradiated to the surface of the object and returned according to the original path, the larger the Radar Cross section area is, the larger the signal characteristic of the Radar to the target is.
In an alternative embodiment, obtaining the relative radar scattering cross-sectional area of the target to be classified comprises S01-S02:
and S01, acquiring a target distance power spectrum, and calculating echo power based on the target distance power spectrum.
In the embodiment of the invention, to obtain a target distance power spectrum, signal data received by a radar antenna array is firstly obtained, namely, an echo signal reflected by a target to be classified is received, and the echo signal is subjected to analog-to-digital conversion and digital filtering; note that, in order to calculate the echo power based on the echo signal, the received echo signal is also converted into an intermediate frequency signal before analog-to-digital conversion and digital filtering are performed. Further, the filtered signal is filteredAnd performing two-dimensional Fourier transform, for example, distance and velocity dimensional Fourier transform, wherein the Fourier transform is optionally to obtain distance data and velocity data between the radar and the target to be classified, and performing constant false alarm detection on the signal data after the Fourier transform to obtain a target distance power spectrum of the target to be classified. Optionally, in actual engineering, the target echo power may be distributed in adjacent distance units, and the obtained target distance power spectrum is denoted as [ P [ ]r1，Pr2，Pr3…PrN]And N is the number of distance units distributed by the target, the target echo power PrComprises the following steps:
and S02, calculating the relative radar scattering sectional area of the target to be classified based on the echo power, the radar system parameters and the distance between the target to be classified and the radar, which is acquired in advance.
Optionally, on the basis of obtaining the echo power, the relative radar scattering cross-sectional area of the target to be classified is calculated according to a preset formula based on the radar system parameters and the distance between the target to be classified and the radar, which is acquired in advance; wherein the preset formula is as follows:
wherein σ is the relative radar scattering cross-sectional area, PrFor the echo power, R is the distance from the target to be classified to the radar, and k (θ, R) is the system parameter of the radar.
It should be noted that the distance from the target to the radar may be obtained from distance data obtained by performing distance dimensional fourier transform on the echo signal; the echo power P received by the radar intermediate frequency receiver after the transmitted signal is scattered by the targetrComprises the following steps:
wherein P istTransmitting power for radar; g (theta) is antenna transmitting and receiving gain and is related to the direction angle theta; l (R) is the power loss, related to the distance R; for the same type of radar system, PtIs constant, G (θ) and l (r) are radar intrinsic parameters. The radar system parameters are thus determined to be k (θ, R), and specific k (θ, R) is as follows:
in the embodiment of the invention, the target distance power spectrum entropy value can be used for expressing the uncertainty of the distance between the target and the radar. The smaller the entropy value is, the smaller the uncertainty of the distance between the target and the radar is; conversely, a larger entropy value indicates a larger uncertainty in the distance between the target and the radar. In an optional embodiment, calculating the target distance power spectrum entropy of the target to be classified comprises S11-S12:
s11, carrying out normalization processing on the echo power to obtain the weight corresponding to each sub-power value in the target distance power spectrum.
From the above, the echo power PrWill be distributed in adjacent distance units and will be marked as Pr1，Pr2，Pr3…PrN]And N is the number of distance units distributed by the target, the target echo power PrComprises the following steps:
when the echo power is normalized, the normalization processing can be performed according to the following formula:
wherein, wiRepresenting the weight corresponding to the ith sub-power value in the target range power spectrum, e.g. w1Represents Pr1And (4) corresponding weight values.
And S12, obtaining a target distance power spectrum entropy value of the target to be classified according to a preset entropy value calculation formula and the weight.
After the weight corresponding to each sub power value in the target distance power spectrum is obtained, the target distance power spectrum entropy value of the target to be classified is obtained based on a preset entropy value calculation formula optionally. Wherein, the preset entropy calculation formula is as follows:
s102, inputting the relative radar scattering cross section area and the target distance spectral line entropy value into a pre-trained target classifier, and determining the class of the target to be classified according to the output result of the target classifier.
In the embodiment of the present invention, in step S101, a relative radar cross-sectional area and an entropy of a target distance spectrum line are obtained, a feature vector formed by the relative radar cross-sectional area and the entropy of the target distance spectrum line is input to a pre-trained target classifier, and a category to which a target to be classified belongs is determined according to an output result of the target classifier.
The target classifier is a radar target classifier based on a vector machine (SVM), wherein a kernel Function of the target classifier is a Radial Basis Function (RBF). Optionally, the radial basis function is as follows:
among them, the nuclear parameters are shown.
It should be noted that the target classifier may also be implemented based on other algorithms, for example, by using neural network training, and is not limited in this respect.
In the embodiment of the invention, the process of training the target classifier is as follows: and collecting the radar cross section area and the target distance power spectrum entropy value of the target which always belongs to the category as a training sample, and training the constructed target classifier based on the training sample.
In the embodiment of the invention, the relative radar cross-section area and the target distance power spectrum entropy of the target to be classified are calculated, the relative radar cross-section area and the target distance spectrum entropy are input into a pre-trained target classifier, and the class of the target to be classified is determined according to the output result. Therefore, two-dimensional characteristics of the target relative radar cross-sectional area and the entropy value based on different classifications are extracted, the target classification is estimated, the accuracy of the target classification can be improved, and the target relative radar cross-sectional area and the entropy value can be directly calculated based on signals received by the radar and a preset formula, so that the calculated amount is reduced, and the calculation complexity of the target classification is further reduced.
Example two
Fig. 2 is a schematic configuration diagram of an object classification device for detecting a preceding vehicle and an obstacle in an on-vehicle adaptive cruise control system according to a second embodiment of the present invention, the device including:
the calculation module 201 is configured to obtain a relative radar scattering cross-sectional area of the target to be classified and a target distance power spectrum entropy value;
the classification module 202 is configured to input the relative radar scattering cross-sectional area and the target distance spectral line entropy into a pre-trained target classifier, and determine a category to which a target to be classified belongs according to an output result of the target classifier.
The method and the device provided by the embodiment of the invention have the advantages that the target classification is estimated based on the two-dimensional characteristics of the scattering cross section area and the entropy value of the target relative to the radar extracted from different classifications, the accuracy of the target classification can be improved, the scattering cross section area and the entropy value of the target relative to the radar can be directly calculated according to signals received by the radar and a preset formula, the calculated amount is reduced, and the calculation complexity of the target classification is further reduced.
On the basis of the foregoing embodiment, optionally, the calculation module includes:
the first calculation unit is used for acquiring a target distance power spectrum and calculating echo power based on the target distance power spectrum;
and the second calculation unit is used for calculating the relative radar scattering sectional area of the target to be classified based on the echo power, the radar system parameters and the distance between the target to be classified and the radar which is acquired in advance.
On the basis of the foregoing embodiment, optionally, the second computing unit is specifically configured to:
calculating the relative radar scattering sectional area of the target to be classified according to a preset formula based on the echo power, the radar system parameters and the distance between the target to be classified and the radar which is acquired in advance; wherein the preset formula is as follows:
wherein σ is the relative radar scattering cross-sectional area, PrFor the echo power, R is the distance from the target to be classified to the radar, and k (θ, R) is the system parameter of the radar.
On the basis of the foregoing embodiment, optionally, the calculation module further includes:
the third calculating unit is used for carrying out normalization processing on the echo power to obtain weights corresponding to all sub power values in the target distance power spectrum;
and the fourth calculating unit is used for obtaining the target distance power spectrum entropy value of the target to be classified according to a preset entropy value calculating formula and the weight.
On the basis of the foregoing embodiment, optionally, the apparatus further includes:
the conversion filtering module is used for receiving echo signals reflected by the target to be classified, and performing analog-to-digital conversion and digital filtering on the echo signals;
and the Fourier transform and constant false alarm detection module is used for carrying out Fourier transform of distance dimension and speed dimension on the filtered signals and carrying out constant false alarm detection on the signal data after the Fourier transform to obtain a target distance power spectrum of the target to be classified.
On the basis of the foregoing embodiment, optionally, the target classifier is a radar target classifier based on a vector machine, where a kernel function of the target classifier is a radial basis function.
The object classification device provided by the embodiment of the invention can execute the object classification method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
EXAMPLE III
Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention. FIG. 3 illustrates a block diagram of an exemplary electronic device 12 suitable for use in implementing embodiments of the present invention. The electronic device 12 shown in fig. 3 is only an example and should not bring any limitations to the function and scope of use of the embodiments of the present invention.
As shown in FIG. 3, electronic device 12 is embodied in the form of a general purpose computing device. The components of electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. The electronic device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 3, and commonly referred to as a "hard drive"). Although not shown in FIG. 3, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
The processing unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, to implement the object classification method provided by the embodiment of the present invention, the method including:
acquiring a relative radar scattering cross section of a target to be classified and a target distance power spectrum entropy value;
and inputting the relative radar scattering cross-section area and the target distance spectral line entropy value into a pre-trained target classifier, and determining the class of the target to be classified according to the output result of the target classifier.
Example four
The fourth embodiment of the present invention further provides a storage medium, in particular a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the object classification method provided in the embodiment of the present invention, where the method includes:
acquiring a relative radar scattering cross section of a target to be classified and a target distance power spectrum entropy value;
and inputting the relative radar scattering cross-section area and the target distance spectral line entropy value into a pre-trained target classifier, and determining the class of the target to be classified according to the output result of the target classifier.
Storage media for embodiments of the present invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having 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. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method of object classification, the method comprising:
acquiring a relative radar scattering cross section of a target to be classified and a target distance power spectrum entropy value;
and inputting the relative radar scattering cross-section area and the target distance spectral line entropy value into a pre-trained target classifier, and determining the class of the target to be classified according to the output result of the target classifier.
2. The method of claim 1, wherein obtaining relative radar cross-sectional areas of the objects to be classified comprises:
acquiring a target distance power spectrum, and calculating echo power based on the target distance power spectrum;
and calculating the relative radar scattering sectional area of the target to be classified based on the echo power, the radar system parameters and the distance between the target to be classified and the radar which is acquired in advance.
3. The method of claim 2, wherein calculating the relative radar scattering cross-sectional area of the target to be classified based on the echo power, radar system parameters and the distance from the target to be classified to the radar, which is obtained in advance, comprises:
calculating the relative radar scattering sectional area of the target to be classified according to a preset formula based on the echo power, the radar system parameters and the distance between the target to be classified and the radar which is acquired in advance; wherein the preset formula is as follows:
wherein σ is the relative radar scattering cross-sectional area, PrFor the echo power, R is the distance from the target to be classified to the radar, and k (θ, R) is the system parameter of the radar.
4. The method of claim 1, wherein calculating a target range power spectrum entropy value for the target to be classified comprises:
carrying out normalization processing on the echo power to obtain the weight corresponding to each sub-power value in the target distance power spectrum;
and obtaining the target distance power spectrum entropy value of the target to be classified according to a preset entropy value calculation formula and the weight.
5. The method of claim 1, wherein prior to calculating a relative radar cross-sectional area and a target range power spectrum entropy value for the target to be classified, the method further comprises:
receiving echo signals reflected by a target to be classified, and performing analog-to-digital conversion and digital filtering on the echo signals;
and carrying out Fourier transformation of distance dimension and speed dimension on the filtered signals, and carrying out constant false alarm detection on signal data subjected to Fourier transformation to obtain a target distance power spectrum of the target to be classified.
6. The method of claim 1, wherein the target classifier is a vector machine-based radar target classifier, wherein a kernel function of the target classifier is a radial basis function.
7. An object classification apparatus, characterized in that the apparatus comprises:
the calculation module is used for acquiring a relative radar scattering cross section of the target to be classified and a power spectrum entropy value of a target distance;
and the classification module is used for inputting the relative radar scattering cross-sectional area and the target distance spectral line entropy value into a pre-trained target classifier and determining the class of the target to be classified according to the output result of the target classifier.
8. The apparatus of claim 7, wherein the computing module comprises:
the first calculation unit is used for acquiring a target distance power spectrum and calculating echo power based on the target distance power spectrum;
and the second calculation unit is used for calculating the relative radar scattering sectional area of the target to be classified based on the echo power, the radar system parameters and the distance between the target to be classified and the radar which is acquired in advance.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the object classification method of any one of claims 1-6.
10. A storage medium on which a computer program is stored which, when being executed by a processor, carries out the object classification method according to any one of claims 1 to 6.
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|---|---|---|---|---|
| CN112782684A (en) * | 2020-12-31 | 2021-05-11 | 深圳大学 | Human body target detection method and device, computer equipment and storage medium |
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