CN112946630A - Personnel counting and tracking method based on millimeter wave radar - 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
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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
- G01S13/66—Radar-tracking systems; Analogous systems
- G01S13/72—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
- G01S13/723—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar by using numerical data
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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
- 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
- G01S7/415—Identification of targets based on measurements of movement associated with the target
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
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Abstract
The invention discloses a personnel counting and tracking method based on a millimeter wave radar, wherein the processing process of each frame data of the millimeter wave radar comprises the following steps: (S1) processing the echo signal of the current frame to obtain a target point cloud matrix of the current frame; (S2) carrying out clustering analysis on the target point cloud matrix to obtain target object clusters corresponding to all target objects; (S3) the status types of the object clusters include suspicious, confirmed, stationary and leaving, in this step, the ID and status of each object cluster in the current frame are determined by combining the previous frame data, and the number of new objects whose status is changed from suspicious to confirmed and the number of leaving objects whose status is changed from confirmed to left are counted. The method increases the suspicious state and the static state of the target and the credibility concept, solves the problem that the target disappears after being static, reduces false detection and improves the reliability of detection.
Description
Technical Field
The invention relates to the field of sensors, in particular to a personnel counting and tracking method based on a millimeter wave radar.
Background
With the development of science and technology, the requirements of factories, cities and the like on intelligent detection and personnel safety protection are increasing day by day. The important ring for realizing intelligent factories and intelligent cities is the detection of personnel and the statistics of the number of the personnel.
The millimeter wave radar can penetrate rain, snow and fog, can stably detect indoors and outdoors, has high sensitivity to micro actions of people in millimeter wave bands, and is an effective means for personnel detection and people counting.
The current people counting method comprises the following steps: passive infrared detectors (PIR), optical cameras, laser radars, etc. And the passive infrared detector detects by sensing the difference between the temperature of the moving object and the background temperature. The method has the advantages of low power consumption and simple technology. The optical camera can analyze the human images and determine the number and the behaviors of the people. The infrared camera can work at night. The laser radar has high angular resolution, and can provide information such as distance, direction and the like of a target to draw a map. The millimeter wave radar has extremely high sensitivity for detecting the actions of a human body, can detect the micro actions of breathing and typing, has a detection distance of dozens of meters, and is not easily influenced by weather and environment.
The prior art has the following disadvantages:
(1) the passive infrared detector is greatly influenced by illumination, temperature and the like outdoors, is easy to generate false detection, and has small measuring range and low action detection sensitivity.
(2) The camera can be influenced by factors such as shadow, shading, light, environment and the like, the algorithm requirement is complex, and the problem of privacy exposure can be caused.
(3) The range of the laser radar is attenuated under outdoor strong light, and the software algorithm is complex and the calculation amount is large.
(4) When the millimeter wave radar detects a target object, when the target position is static, the detection cannot be performed, and the personnel counting is wrong. And a clustering algorithm based on density is generally adopted, so that the calculation amount is large. Noise interference also easily causes personnel false detection.
Disclosure of Invention
The invention aims to provide a personnel counting and tracking method based on a millimeter wave radar, which clusters through detection results of the millimeter wave radar to obtain target object clusters of all target objects; and the state of the target object is converted and judged according to the detection result of each frame, so that personnel counting and tracking are realized, and the problems in the prior art are solved.
The technical scheme of the invention is that the personnel counting and tracking method based on the millimeter wave radar comprises the following steps of:
(S1) processing the echo signal of the current frame to obtain a target point cloud matrix of the current frame;
(S2) carrying out clustering analysis on the target point cloud matrix to obtain target object clusters corresponding to all target objects;
(S3) the status types of the object clusters include suspicious, confirmed, stationary and leaving, in this step, the ID and status of each object cluster in the current frame are determined by combining the previous frame data, and the number of new objects whose status is changed from suspicious to confirmed and the number of leaving objects whose status is changed from confirmed to left are counted.
The invention has the further improvement that the signals transmitted by the millimeter wave radar are 76-81 GHz millimeter waves, sawtooth wave frequency modulation is adopted, and the frequency modulation bandwidth is 5 GHz.
The invention has the further improvement that in the process of receiving the echo signal by the millimeter wave radar, the echo signal is received by the receiving antenna; and the received signal and the transmitted signal are subjected to frequency mixing to obtain an intermediate frequency signal, and the intermediate frequency signal is acquired by the MCU.
A further improvement of the present invention resides in that the step (S1) includes:
(S11) performing one-dimensional FFT on the intermediate frequency signal, extracting distance-dimensional information; and performing two-dimensional FFT on the one-dimensional FFT results of the multiple frequency modulation periods, extracting speed dimension information and generating an R-V matrix.
(S12) extracting a target point from the R-V matrix by using a CFAR algorithm to generate a target point cloud matrix;
(S13) calculating the target point angle by using the multi-channel receiving antenna signal phase difference, tracking the target by using Kalman filtering to obtain the motion parameter, and updating the motion parameter of the target point cloud matrix.
A further improvement of the present invention resides in that the step (S2) includes: establishing a target point cloud matrix according to the limit volume of the target object by using a speed ascending standard; preliminarily classifying the speed in the target point cloud matrix, and extracting the classified distance and speed midpoint as the centroid of the target object cluster; and adding the centroid and the adjacent target points into the target object cluster, thereby obtaining the target object cluster corresponding to each target object.
The further improvement of the invention lies in that in the process of determining the ID and the state of each target cluster in the current frame:
if a certain target object cluster in the current frame has no corresponding target object cluster in the previous frame data, setting the state of the target object cluster as suspicious;
if a certain target cluster in the current frame has a corresponding suspicious target cluster in the previous frame of data, setting the state of the target cluster as suspicious and increasing the credibility of the target cluster; when the credibility is greater than the credibility threshold, converting the state of the target object into a determined and configured ID, and adding one to the number of the newly added target objects;
if a certain determined or static target cluster in the previous frame data does not have a corresponding target cluster in the current frame and the prediction range of the target cluster is located in the detection area, the target cluster is reserved in the current frame and the state of the target cluster is converted into static;
if a certain target object cluster in the current frame corresponds to a certain static target object cluster in the previous frame data and the speed of the target object cluster reaches a motion threshold, setting the state of the target object cluster as a determination;
if a certain target cluster in the current frame corresponds to a certain determined target cluster in the previous frame data, setting the state of the target cluster as determined, and setting the ID of the target cluster as the ID of the corresponding target cluster;
if a certain target cluster in the previous frame data does not have a corresponding target cluster in the current frame and the prediction range of the target cluster is located outside the detection area, the state of the target is converted to leave, the ID of the target is cancelled, and the number of leaving targets is increased by one.
A further refinement of the invention consists in that each target cluster comprises a plurality of target points and motion parameters of the target points; in step S3, a kalman filter tracking algorithm is used to determine whether a target cluster in the previous frame of data corresponds to a target cluster in the current frame of data.
The invention is further improved in that in the current frame, if the duration of a certain target cluster in a static state is greater than a timeout threshold, the target is deleted, and the corresponding ID is cancelled.
The invention has the beneficial effects that:
1) the millimeter wave radar is adopted to carry out personnel detection, tracking and people counting, and the privacy problem is not involved.
2) The target identification method of extracting the mass center by using the median and filling the target cluster matrix has simple algorithm and small calculated amount.
3) The suspicious state and the static state of the target and the credibility concept are increased, the problem that the target disappears after being static is solved, meanwhile, false detection is reduced, and the detection reliability is improved.
4) And predicting and matching the target at the next moment through a Kalman filtering tracking algorithm to realize the function of tracking the target object.
Drawings
FIG. 1 is a flow chart of a personnel counting and tracking method based on millimeter wave radar according to the invention;
FIG. 2 is a schematic diagram of a millimeter wave radar system for use with the present invention;
FIG. 3 is a schematic diagram of the acquisition process of a target cluster matrix;
FIG. 4 is a flow chart of a process of acquiring a point cloud matrix.
Detailed Description
As shown in fig. 1 and 2, an embodiment of the present invention provides a method for counting and tracking people based on a millimeter wave radar, and the millimeter wave radar used in the method is composed of a radio frequency front end, a millimeter wave radar chip, a processor and an upper computer. The radar chip controls the radio frequency front end to transmit and receive signals and perform frequency mixing processing, the processor collects the signals, performs signal resolving and related algorithms, and sends processing results to the upper computer to be displayed. The signal transmitted by the millimeter wave radar is a millimeter wave of 76-81 GHz, sawtooth frequency modulation is adopted, and the frequency modulation bandwidth is 5 GHz. Detection zone 6 m. In the process of receiving echo signals by the millimeter wave radar, receiving the echo signals by a receiving antenna; and the received signal and the transmitted signal are subjected to frequency mixing to obtain an intermediate frequency signal, and the intermediate frequency signal is acquired and processed by the MCU. The millimeter wave radar periodically scans the detection area, one frame of data is obtained by scanning each time, and the processing process of each frame of data of the millimeter wave radar comprises the following steps:
(S1) processing the echo signal of the current frame to obtain a target point cloud matrix of the current frame. The method specifically comprises the following steps:
(S11) performing one-dimensional FFT on the intermediate frequency signal, extracting distance-dimensional information; and performing two-dimensional FFT on the one-dimensional FFT results of a plurality of frequency modulation periods, extracting speed dimension information, and generating an R-V matrix (distance-speed matrix).
(S12) extracting a target point from the R-V matrix by using a CFAR algorithm to generate a target point cloud matrix;
(S13) calculating the target point angle by using the multi-channel receiving antenna signal phase difference, tracking the target by using Kalman filtering to obtain the motion parameter, and updating the motion parameter of the target point cloud matrix.
(S2) carrying out cluster analysis on the target point cloud matrix to obtain target object clusters corresponding to the target objects. As shown in fig. 3 and 4, in this step, a target point cloud matrix is established according to the limit volume of the target object and the standard of ascending speed; preliminarily classifying the speed in the target point cloud matrix, and extracting the classified distance and speed midpoint as the centroid of the target object cluster; and adding the centroid and the adjacent target points into the target object cluster, thereby obtaining the target object cluster corresponding to each target object.
(S3) the status types of the object clusters include suspicious, confirmed, stationary and leaving, in this step, the ID and status of each object cluster in the current frame are determined by combining the previous frame data, and the number of new objects whose status is changed from suspicious to confirmed and the number of leaving objects whose status is changed from confirmed to left are counted.
Specifically, in the process of determining the ID and the state of each target cluster in the current frame:
if a certain target object cluster in the current frame has no corresponding target object cluster in the previous frame data, setting the state of the target object cluster as suspicious;
if a corresponding state of a certain target cluster in the current frame in the previous frame data is a suspicious target cluster, setting the state of the target cluster as suspicious and increasing the reliability of the target cluster; when the credibility is greater than the credibility threshold, converting the state of the target object into a determined and configured ID, and adding one to the number of the newly added target objects;
if a certain determined or static target cluster in the previous frame data does not have a corresponding target cluster in the current frame and the prediction range of the target cluster is located in the detection area, the target cluster is reserved in the current frame and the state of the target cluster is converted into static; if the duration time of a certain target cluster in a static state is greater than a timeout threshold, deleting the target and cancelling the corresponding ID;
if a certain target object cluster in the current frame corresponds to a certain static target object cluster in the previous frame data and the speed of the target object cluster reaches a motion threshold, setting the state of the target object cluster as a determination;
if a certain target object cluster in the current frame corresponds to a certain determined target object cluster in the previous frame data, setting the state of the target object cluster as determined, and setting the ID as the ID of the corresponding target object cluster;
if a certain target cluster in the previous frame data does not have a corresponding target cluster in the current frame and the prediction range of the target cluster is located outside the detection area, the state of the target is converted to leave, the ID of the target is cancelled, and the number of leaving targets is increased by one.
In this embodiment, each target cluster includes a plurality of target points and motion parameters of the target points; in step S3, a kalman filter tracking algorithm is used to determine whether a certain target cluster in the previous frame of data corresponds to a certain target cluster in the current frame of data, where two target clusters correspond to each other and mean that the two target clusters are two target clusters formed by the same target in two frames of data. Kalman filter tracking algorithms are known in the art.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (8)
1. A personnel counting and tracking method based on a millimeter wave radar is characterized in that the processing process of each frame data of the millimeter wave radar comprises the following steps:
(S1) processing the echo signal of the current frame to obtain a target point cloud matrix of the current frame;
(S2) carrying out clustering analysis on the target point cloud matrix to obtain target object clusters corresponding to all target objects;
(S3) the status types of the object clusters include suspicious, confirmed, stationary and leaving, in this step, the ID and status of each object cluster in the current frame are determined by combining the previous frame data, and the number of new objects whose status is changed from suspicious to confirmed and the number of leaving objects whose status is changed from confirmed to left are counted.
2. The people counting and tracking method based on the millimeter wave radar as claimed in claim 1, wherein the signal transmitted by the millimeter wave radar is 76-81 GHz millimeter wave, sawtooth frequency modulation is adopted, and the frequency modulation bandwidth is 5 GHz.
3. The people counting and tracking method based on the millimeter wave radar as claimed in claim 1, wherein in the process of receiving the echo signal by the millimeter wave radar, the echo signal is received by a receiving antenna; and the received signal and the transmitted signal are subjected to frequency mixing to obtain an intermediate frequency signal, and the intermediate frequency signal is acquired by the MCU.
4. The millimeter wave radar-based people counting and tracking method according to claim 3, wherein the step (S1) comprises:
(S11) performing one-dimensional FFT on the intermediate frequency signal, extracting distance-dimensional information; and performing two-dimensional FFT on the one-dimensional FFT results of the multiple frequency modulation periods, extracting speed dimension information and generating an R-V matrix.
(S12) extracting a target point from the R-V matrix by using a CFAR algorithm to generate a target point cloud matrix;
(S13) calculating the target point angle by using the multi-channel receiving antenna signal phase difference, tracking the target by using Kalman filtering to obtain the motion parameter, and updating the motion parameter of the target point cloud matrix.
5. The millimeter wave radar-based people counting and tracking method according to claim 4, wherein the step (S2) comprises: establishing a target point cloud matrix according to the limit volume of the target object by using a speed ascending standard; preliminarily classifying the speed in the target point cloud matrix, and extracting the classified distance and speed midpoint as the centroid of the target object cluster; and adding the centroid and the adjacent target points into the target object cluster, thereby obtaining the target object cluster corresponding to each target object.
6. The millimeter wave radar-based personnel counting and tracking method according to claim 1, wherein in the process of determining the ID and the state of each target cluster in the current frame:
if a certain target object cluster in the current frame has no corresponding target object cluster in the previous frame data, setting the state of the target object cluster as suspicious;
if a certain target cluster in the current frame has a corresponding suspicious target cluster in the previous frame of data, setting the state of the target cluster as suspicious and increasing the credibility of the target cluster; when the credibility is greater than the credibility threshold, converting the state of the target object into a determined and configured ID, and adding one to the number of the newly added target objects;
if a certain determined or static target cluster in the previous frame data does not have a corresponding target cluster in the current frame and the prediction range of the target cluster is located in the detection area, the target cluster is reserved in the current frame and the state of the target cluster is converted into static;
if a certain target object cluster in the current frame corresponds to a certain static target object cluster in the previous frame data and the speed of the target object cluster reaches a motion threshold, setting the state of the target object cluster as a determination;
if a certain target cluster in the current frame corresponds to a certain determined target cluster in the previous frame data, setting the state of the target cluster as determined, and setting the ID of the target cluster as the ID of the corresponding target cluster;
if a certain target cluster in the previous frame data does not have a corresponding target cluster in the current frame and the prediction range of the target cluster is located outside the detection area, the state of the target is converted to leave, the ID of the target is cancelled, and the number of leaving targets is increased by one.
7. The millimeter wave radar-based people counting and tracking method according to claim 6, wherein each target cluster comprises a plurality of target points and motion parameters of the target points; in step S3, a kalman filter tracking algorithm is used to determine whether a target cluster in the previous frame of data corresponds to a target cluster in the current frame of data.
8. The people counting and tracking method based on millimeter wave radar as claimed in claim 6, wherein in the current frame, if the duration of a certain target cluster in a static state is longer than a timeout threshold, the target is deleted and the corresponding ID is cancelled.
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| CN113740838A (en) * | 2021-09-13 | 2021-12-03 | 四川启睿克科技有限公司 | Whole-house personnel tracking method based on millimeter wave radar |
| CN113835074A (en) * | 2021-08-04 | 2021-12-24 | 南京常格科技发展有限公司 | People flow dynamic monitoring method based on millimeter wave radar |
| CN115128571A (en) * | 2022-09-02 | 2022-09-30 | 长沙莫之比智能科技有限公司 | Multi-person and non-motor vehicle identification method based on millimeter wave radar |
| CN117908003A (en) * | 2024-03-19 | 2024-04-19 | 清澜技术(深圳)有限公司 | Space people counting method and system |
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