CN114580473A - Radar-based human behavior identification method and system - Google Patents
Radar-based human behavior identification method and system Download PDFInfo
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Abstract
The invention provides a human behavior identification method and a system based on radar, which comprises the following steps: a signal acquisition step: the radar wave beam radiates a detected human body to obtain a baseband signal of the radar; a signal processing step: preprocessing the baseband signals, extracting the characteristics of the preprocessed baseband signals to obtain characteristic indexes of human behaviors, and classifying the human behaviors. The invention is suitable for the human behavior recognition of all-time, non-contact and no privacy disclosure risk, and can recognize: the total 6 common human body behaviors of walking-falling, standing-falling, normal walking, standing-swinging, standing-sitting, walking-sitting and the like solve the problems of difficult resolution and error resolution of similar actions such as falling, sitting and the like, and can realize high-precision recognition effect of small sample size based on feature driving; and training the model by using a small amount of sample data as a training set to obtain a high-efficiency and high-precision training model and realize high-accuracy behavior recognition.
Description
Technical Field
The invention relates to the technical field of human behavior identification, in particular to a radar-based human behavior identification method and system. In particular, the invention preferably relates to an indoor human body behavior identification method and system based on a millimeter wave radar.
Background
In recent years, human activity recognition technology is becoming more and more important in our daily life, for example, falls are considered as more important human activity information. Research shows that the fall of the old people approaching 1/3 occurs every year, and the life quality of the old people is seriously influenced. And the development rule can be counted by observing other human behavior information for a long time, and the physical and psychological health conditions of the human body can be analyzed.
Human behavior recognition techniques can be broadly classified into: contact and contactless. Wherein the touch sensor is mainly an acceleration sensor. The non-contact sensor mainly includes a camera, a radar, a microphone, and the like.
The Chinese invention patent document with the publication number of CN113869189A discloses a human behavior recognition method, a system, equipment and a medium, which belong to the field of data retrieval, and the method comprises the following steps: capturing an RGB video sequence, an acceleration signal and an angular velocity signal of a human body in a target area, and extracting video characteristics, acceleration characteristics and angular velocity characteristics related to human body behavior recognition in the RGB video sequence, the acceleration signal and the angular velocity signal; performing multi-sensor signal fusion processing on a cyclic matrix formed by the acceleration characteristics and a cyclic matrix formed by the angular velocity characteristics to obtain an inertial sensor fusion characteristic vector; performing bimodal fusion based on Tak decomposition on the inertial sensor fusion feature vector and the video feature to obtain fusion behavior features; and inputting the fusion behavior characteristics into a classifier to perform human behavior recognition so as to predict and output human actions.
In view of the above-mentioned related art, the inventor believes that when a touch sensor is used, the touch sensor is easily damaged because it needs to be worn for a long time, and the sensor is easily lost, and the false alarm rate of the sensor is high. When the non-contact sensor is used, privacy leakage is easily caused by a camera and a microphone in the non-contact sensor, the camera has high requirement on illumination conditions and cannot be used in an environment with weak illumination conditions, and the microphone has poor anti-interference performance and is easy to receive environmental interference.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a human body behavior identification method and system based on radar.
The invention provides a human body behavior identification method based on radar, which comprises the following steps:
a signal acquisition step: the radar wave beam radiates a detected human body to obtain a baseband signal of the radar;
a signal processing step: preprocessing the baseband signals, extracting the characteristics of the preprocessed baseband signals to obtain characteristic indexes of human behaviors, and classifying the human behaviors according to the characteristic indexes of the human behaviors.
Preferably, the signal acquiring step includes the steps of:
a signal generating step: generating a radar input signal;
a shunting step: dividing the radar input signal into a first radar input signal and a second radar input signal;
a receiving and transmitting step: transmitting a first radar input signal, wherein the first radar input signal meets the reflection of a human body and receives the reflected first radar input signal;
a frequency mixing step: and mixing the second radar input signal and the reflected first radar input signal to obtain the baseband signal.
Preferably, the method further comprises the step of analog-to-digital conversion: converting the baseband signal into a digital signal;
in the signal processing step, the digital signals are processed, and the preprocessed digital signals are subjected to feature extraction to obtain feature indexes of human behaviors.
Preferably, the signal processing step includes a baseband signal preprocessing step, a feature extraction step and a classification step;
the baseband signal preprocessing step includes an image acquisition step: carrying out domain analysis on the baseband signals to obtain distance time domain images and micro-Doppler images of the baseband signals;
the characteristic extraction step comprises the following steps: extracting the characteristics of the human body in the distance time domain image and the micro Doppler image to obtain characteristic indexes of human body behaviors;
the classification step comprises: and classifying the human body behaviors according to the characteristic indexes of the human body behaviors.
Preferably, the characteristic indexes of the human body behavior include an energy gradient, a loss duration, a maximum displacement, a rest duration and an average zero crossing rate.
Preferably, the baseband signal preprocessing step further comprises a clutter suppression step: static clutter in the distance time domain image is suppressed by a variable-pitch static clutter suppression method;
in the characteristic extraction step, the characteristic extraction is carried out on the human body in the micro Doppler image and the distance time domain image after static clutter suppression, and the characteristic index of the human body behavior is obtained.
Preferably, the baseband signal preprocessing step further includes a ridge line correction step: extracting the ridge line of the human body in the distance time domain image after the static clutter suppression, correcting the ridge line of the human body according to an energy threshold value, and putting the corrected ridge line into the distance time domain image;
in the characteristic extraction step, the characteristic extraction is carried out on the human body in the distance time domain image and the micro Doppler image after the ridge line is corrected, and the characteristic index of the human body behavior is obtained.
The invention provides a human behavior recognition system based on a radar, which comprises the radar and a signal processing module;
the radar radiates a detected human body through radar beams to obtain a baseband signal of the radar;
the signal processing module is used for preprocessing the baseband signals, extracting the characteristics of the preprocessed baseband signals to obtain the characteristic indexes of the human behaviors, and classifying the human behaviors according to the characteristic indexes of the human behaviors.
Preferably, the radar comprises a transceiving antenna, a mixer, a power divider and a signal generator;
the signal generator generates a radar input signal;
the power divider divides the radar input signal into a first radar input signal and a second radar input signal;
the receiving and transmitting antenna transmits a first radar input signal, the first radar input signal meets the reflection of a human body, and the reflected first radar input signal is received;
and the mixer mixes the second radar input signal and the reflected first radar input signal to obtain the baseband signal.
Preferably, the radar further comprises an analog-to-digital converter for converting the baseband signal into a digital signal;
the signal processing module is a digital signal processing module; the digital signal processing module is used for preprocessing the digital signals, extracting features according to the preprocessed digital signals to obtain feature indexes of human behaviors, and classifying the human behaviors according to the feature indexes of the human behaviors.
Compared with the prior art, the invention has the following beneficial effects:
1. the method is suitable for identifying the human behavior all day long, in a non-contact manner and without privacy disclosure risk;
2. the invention can identify: the total 6 common human body behaviors of walking-falling, standing-falling, normal walking, standing-swinging hands, standing-sitting and walking-sitting solve the problem of difficult resolution and error resolution of similar actions such as falling and sitting;
3. the invention can realize high-precision identification effect of small sample size based on feature driving; and training the model by using a small amount of sample data as a training set to obtain a high-efficiency and high-precision training model and realize high-accuracy behavior recognition.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a system block diagram;
FIG. 2 is a time-distance graph;
FIG. 3 is a micro-Doppler plot;
FIG. 4 is a schematic diagram of a static clutter suppression algorithm;
FIG. 5 is a schematic diagram of a static clutter suppressed image;
FIG. 6 is a graph showing the energy at the ridge line of the target object;
FIG. 7 is a schematic view of a ridge before correction;
FIG. 8 is a schematic view of a corrected ridge line;
FIG. 9 is a diagram of a decision tree model.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
The embodiment of the invention discloses an indoor human body behavior identification method based on a millimeter wave radar, which comprises the following steps as shown in figure 1: a signal acquisition step: the radar wave beam radiates the detected human body to obtain the baseband signal of the radar.
The signal acquisition step includes the steps of:
a signal generating step: generating a radar input signal;
a shunting step: the radar input signal is split into a first radar input signal and a second radar input signal.
A receiving and transmitting step: a first radar input signal is transmitted, meets the reflection of a human body, and receives the reflected first radar input signal.
A frequency mixing step: and mixing the reflected first radar input signal and the second radar input signal to obtain a baseband signal.
An analog-digital conversion step: the baseband signal is converted into a digital signal.
A signal processing step: preprocessing the baseband signals, extracting the characteristics of the preprocessed baseband signals to obtain characteristic indexes of human behaviors, and classifying the human behaviors according to the characteristic indexes of the human behaviors. Namely classifying human body behaviors based on a multi-feature fusion method.
The method comprises the steps of preprocessing a digital signal, extracting features according to the preprocessed digital signal, and extracting the features of the preprocessed digital signal to obtain the feature index of human behavior. The characteristic indexes of the human body behavior comprise energy gradient, loss duration, maximum displacement, rest duration and average zero crossing rate.
The signal processing step comprises a baseband signal preprocessing step, a feature extraction step and a classification step.
The baseband signal preprocessing step comprises an image acquisition step, a clutter suppression step and a ridge line correction step. An image acquisition step: and carrying out domain analysis on the baseband signals to obtain distance time domain images and micro Doppler images of the baseband signals. Clutter suppression step: and (3) suppressing the static clutter in the distance time domain image by a variable-pitch static clutter suppression method.
Ridge line correction: and extracting the ridge line of the human body in the distance time domain image after the static clutter suppression, correcting the ridge line of the human body according to an energy threshold, and putting the corrected ridge line into the distance time domain image.
A characteristic extraction step: and (4) extracting the characteristics of the human body in the micro Doppler image and the distance time domain image after the ridge line is corrected to obtain the characteristic indexes of the human body behaviors.
And (4) classification: and classifying the human body behaviors according to the characteristic indexes of the human body behaviors.
The embodiment of the invention also discloses an indoor human body behavior recognition system based on the millimeter wave radar, which comprises a radar and a signal processing module as shown in figure 1. The radar radiates a detected human body through radar beams to obtain a baseband signal of the radar.
The radar includes a transmitting and receiving antenna, a mixer, a power divider, a signal generator, a filter, a signal amplifier, and an analog-to-digital converter (AD converter). The signal amplifier comprises a power amplifier and a low noise amplifier. The transceiving antenna comprises a transmitting antenna and a receiving antenna. The AD converter is abbreviated as ADC, and is called analog to digital converter, and the chinese translation is an analog-to-digital converter.
The signal generator generates a radar input signal. The power divider divides the radar input signal into a first radar input signal and a second radar input signal. The power divider is: dividing one path of radar input signal energy into two paths which are respectively used for a transmitting antenna and a mixer.
The power amplifier is used for amplifying the first radar input signal.
The receiving and transmitting antenna transmits the amplified first radar input signal, and the first radar input signal meets the reflection of a human body and receives the reflected first radar input signal. The transceiving antenna is used for transmitting and receiving radar signals. The transmitting antenna is used for transmitting the amplified first radar input signal; the receiving antenna is used for receiving the first radar input signal reflected when the first radar input signal meets the reflection of a human body.
The low noise amplifier is used for amplifying the reflected radar input signal.
And the mixer receives the second radar input signal, and mixes the amplified and reflected first radar input signal and the second radar input signal to obtain a baseband signal. A mixer: the transmit signal (second radar input signal) and the echo signal (reflected first radar input signal) are mixed to obtain a baseband signal for subsequent data processing.
The filter comprises a low-pass filter; the low pass filter is used for filtering the baseband signal.
The analog-to-digital converter converts the filtered baseband signal into a digital signal.
Signal processing step of the signal processing module: preprocessing the baseband signals, extracting the characteristics of the preprocessed baseband signals to obtain characteristic indexes of human behaviors, and classifying the human behaviors according to the characteristic indexes of the human behaviors.
The signal processing module is a digital signal processing module; the digital signal processing module is used for preprocessing the digital signals, extracting characteristics according to the preprocessed digital signals to obtain characteristic indexes of human behaviors, and classifying the human behaviors according to the characteristic indexes of the human behaviors. The digital signal processing module: according to the baseband signals of the radar, the signals are processed, the characteristics of the signals are extracted, and the signals are classified.
The invention provides an indoor human body line identification method and system based on a millimeter wave radar, which are all-time, non-contact and free of privacy disclosure risks. An indoor human behavior recognition system based on millimeter wave radar is shown in fig. 1 below. The indoor human body line identification method and system based on the millimeter wave radar comprise an indoor human body line identification method based on the millimeter wave radar and an indoor human body line identification system based on the millimeter wave radar.
The second embodiment of the invention also provides an indoor human behavior identification method based on the millimeter wave radar, which comprises the following steps: data preprocessing, feature extraction and behavior classification.
The specific method comprises the following steps: step 1: firstly, a radar is used for acquiring a baseband signal of human body behavior, and the baseband signal is subjected to domain analysis to acquire distance-time domain information (distance-time domain image) and micro Doppler information (micro Doppler image) of the baseband signal.
Specifically, the radar signal is mixed to obtain a baseband signal SB(t), which can be expressed as:
wherein t represents time;
representing an initial phase; exp represents an exponential function; pi represents a circumferential ratio; f. ofb＝2BRT/cT，fbRepresenting the beat frequency, which is the difference of the two signal frequencies mixed by the mixer; b represents a bandwidth; rTRepresenting the range of the target from the radar; c represents the propagation velocity of the electromagnetic wave, and T represents the duration of the sweep period. Therefore, the target range image at the moment can be obtained by performing fast Fourier transform on each frequency sweep period of the baseband signal, and the range-time image can be obtained by performing short-time Fourier transform on all frequency sweep periods. As shown in fig. 2, distance-time images of back and forth movements, Range represents distance; time represents time. Distance to each momentAnd performing short-time Fourier transform on the sum of the separated images to obtain a micro Doppler image. Figure 3 shows a standing and waving micro-doppler image. Frequency represents Frequency, Sec is fully called second in English, and Chinese translation is second.
Step 2: according to the experimental characteristics, a variable-pitch static clutter elimination method is provided and used for inhibiting and eliminating static clutter in a visual field range. The field of view is the detection area that the radar can reach. Static clutter, for example, a person walking around a room, creates a curve in the range-time image, and if there is a large stationary metallic reflecting object in the room, a straight line appears in the range-time image.
Specifically, in the experimental process, static clutter affects the identification of a target, so that the static clutter must be suppressed. The method can be expressed as follows:
Rb(i)＝Rb(i)-Rb(i+n(i)) (0.2)
wherein R isb(i) Distance image, R, representing the ith sweep periodb(i) Represents a column of data on the range image, i.e., the range-time image, and i represents data that is a column. n (i) represents the distance between the reference range profile and the current range profile, n (i) being variable as a function of i, whose value can be determined according to the following formula:
where th is a fixed value, we take th to be 0.5/T, n represents a time variable, and th is an upper limit value of n. Distance-time images before and after variable-pitch static clutter suppression are shown in fig. 4 and 5.
And step 3: and extracting a target (human body) ridge line in the distance-time domain image, and correcting the target ridge line according to an energy threshold value. The energy threshold is determined by the environment. Generally, the energy threshold is set according to the following relation that after the energy on the ridge line of the target object is arranged from high to low, the area enclosed by the rearranged ridge line is 1: the position of 9 is chosen for the energy. As shown in FIG. 6, the normalized amplitude of the translation in normalized amplitude is shown.
Specifically, after static clutter suppression, we can clearly see the distance change of the target, then we extract the target track (i.e., the ridge), correct the ridge according to the energy threshold, correct the distance where the energy on the ridge is less than the energy threshold to the position of the previous time, and the ridge before and after correction is as shown in fig. 7 and 8.
And 4, step 4: carrying out feature extraction on the target, wherein the extracted features comprise: and obtaining the characteristic indexes of the radar data by energy gradient, loss time length, maximum displacement, static time length and average zero crossing rate.
Specifically, the range-time image and the micro-doppler image of the target are analyzed, and 5 features of the energy gradient, the lost time, the maximum displacement, the static time and the average zero crossing rate of the target are respectively extracted. The energy gradient and the loss duration are mainly used for distinguishing falling and non-falling, the maximum displacement is mainly used for distinguishing whether the movement has displacement or not, the rest duration is mainly used for distinguishing whether the movement contains a rest state or not, and the average zero crossing rate is mainly used for distinguishing standing-sitting and standing-swinging behaviors. Energy gradients, loss duration, maximum displacement, rest duration are extracted from the range-time image. The average zero-crossing rate is extracted from the dimensional doppler image. Distinguishing falling and non-falling, distinguishing whether the activity has displacement, distinguishing whether the activity contains a static state, and distinguishing standing-sitting and standing-waving by using a threshold value obtained by training of an SVM (support vector machine). The SVM is called Support Vector Machine in English, and the Chinese translation is a Support Vector Machine.
And 5: and putting the characteristic indexes of the radar signals into a designed decision tree, and classifying the radar signals according to the size of each characteristic index.
The specific implementation method comprises the following steps: step 5.1: 6 behavior data of 6 test persons were collected, and each behavior of each person was tested 6 times in duplicate, resulting in a total of 216 sets of experimental data.
And step 5.2: and (3) performing the steps 1 to 4 on the 216 groups of experiments, and acquiring the characteristic indexes of each group of data to form a characteristic data set.
Step 5.3: based on the characteristics of each feature, a decision tree structure is designed, and the decision tree structure is shown in fig. 9.
Step 5.4: and training each layer of the decision tree by using a support vector machine and a feature data set to obtain a decision tree model for classification.
Step 5.5: and putting the characteristic indexes of the radar signals into a decision tree model, classifying the radar signals, and acquiring the category of the behavior.
Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, units provided by the present invention as pure computer readable program code, the system and its various devices, modules, units provided by the present invention can be fully implemented by logically programming method steps in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units included in the system for realizing various functions can also be regarded as structures in the hardware component; means, modules, units for performing the various functions may also be regarded as structures within both software modules and hardware components for performing the method.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. A human behavior recognition method based on radar is characterized by comprising the following steps:
a signal acquisition step: the radar wave beam radiates a detected human body to obtain a baseband signal of the radar;
a signal processing step: preprocessing the baseband signals, extracting the characteristics of the preprocessed baseband signals to obtain characteristic indexes of human behaviors, and classifying the human behaviors according to the characteristic indexes of the human behaviors.
2. The radar-based human behavior recognition method according to claim 1, wherein the signal acquisition step comprises the steps of:
a signal generating step: generating a radar input signal;
a shunting step: dividing the radar input signal into a first radar input signal and a second radar input signal;
a receiving and transmitting step: transmitting a first radar input signal, wherein the first radar input signal meets human body reflection and receives the reflected first radar input signal;
a frequency mixing step: and mixing the second radar input signal and the reflected first radar input signal to obtain the baseband signal.
3. The radar-based human behavior recognition method of claim 1, further comprising an analog-to-digital conversion step of: converting the baseband signal into a digital signal;
in the signal processing step, the digital signals are preprocessed, and feature extraction is carried out on the preprocessed digital signals to obtain feature indexes of human behaviors.
4. The radar-based human behavior recognition method according to claim 1, wherein the signal processing step includes a baseband signal preprocessing step, a feature extraction step, and a classification step;
the baseband signal preprocessing step includes an image acquisition step: carrying out domain analysis on the baseband signals to obtain distance time domain images and micro Doppler images of the baseband signals;
the characteristic extraction step comprises the following steps: extracting the characteristics of the human body in the distance time domain image and the micro Doppler image to obtain characteristic indexes of human body behaviors;
the classification step comprises: and classifying the human body behaviors according to the characteristic indexes of the human body behaviors.
5. The radar-based human behavior recognition method according to claim 4, wherein the characteristic indicators of the human behavior include an energy gradient, a loss duration, a maximum displacement, a rest duration, and an average zero crossing rate.
6. The radar-based human behavior recognition method of claim 4, wherein the baseband signal preprocessing step further comprises a clutter suppression step: static clutter in the distance time domain image is suppressed by a variable-pitch static clutter suppression method;
in the characteristic extraction step, the characteristic extraction is carried out on the human body in the micro Doppler image and the distance time domain image after static clutter suppression, and the characteristic index of the human body behavior is obtained.
7. The radar-based human behavior recognition method of claim 6, wherein the baseband signal preprocessing step further comprises a ridge line modification step: extracting the ridge line of the human body in the distance time domain image after the static clutter suppression, correcting the ridge line of the human body according to an energy threshold value, and putting the corrected ridge line into the distance time domain image;
in the characteristic extraction step, the characteristic extraction is carried out on the human body in the micro Doppler image and the distance time domain image after the ridge line is corrected, and the characteristic index of the human body behavior is obtained.
8. A radar-based human behavior recognition system, which is characterized in that the radar-based human behavior recognition method of any one of claims 1 to 7 is applied, and comprises a radar and a signal processing module;
the radar radiates a detected human body through radar beams to obtain a baseband signal of the radar;
the signal processing module is used for preprocessing the baseband signals, extracting the characteristics of the preprocessed baseband signals to obtain the characteristic indexes of the human behaviors, and classifying the human behaviors according to the characteristic indexes of the human behaviors.
9. The radar-based human behavior recognition system of claim 8, wherein the radar includes a transceiving antenna, a mixer, a power divider, and a signal generator;
the signal generator generates a radar input signal;
the power divider divides the radar input signal into a first radar input signal and a second radar input signal;
the receiving and transmitting antenna transmits a first radar input signal, the first radar input signal meets the reflection of a human body, and the reflected first radar input signal is received;
and the mixer mixes the second radar input signal and the reflected first radar input signal to obtain the baseband signal.
10. The radar-based human behavior recognition system of claim 8, wherein the radar comprises an analog-to-digital converter: converting the baseband signal into a digital signal;
the signal processing module is a digital signal processing module; the digital signal processing module is used for preprocessing the digital signals, extracting features according to the preprocessed digital signals to obtain feature indexes of human behaviors, and classifying the human behaviors according to the feature indexes of the human behaviors.
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