KR102034559B1 - Appartus and method for monitoring security using variation of correlation coefficient pattern in sound field spectra - Google Patents
Appartus and method for monitoring security using variation of correlation coefficient pattern in sound field spectra Download PDFInfo
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- KR102034559B1 KR102034559B1 KR1020140146266A KR20140146266A KR102034559B1 KR 102034559 B1 KR102034559 B1 KR 102034559B1 KR 1020140146266 A KR1020140146266 A KR 1020140146266A KR 20140146266 A KR20140146266 A KR 20140146266A KR 102034559 B1 KR102034559 B1 KR 102034559B1
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Abstract
According to an aspect of the present invention, there is provided a security monitoring method comprising: outputting a multitone sound wave composed of a linear sum of sinusoids having a plurality of frequency components into a security monitoring space; Calculating a sound field from the multitone sound waves received in the security surveillance space; Storing reference sound field information for each frequency through the sound field; Calculating sound field information for each current frequency, and then determining whether a sound field change occurs by comparing the reference sound field information for each frequency with the calculated sound field information; Analyzing whether the sound field change has been collected for a period of time, and distinguishing at least two situations from intrusion, motion, and temperature change based on a correlation between a reference sound field spectrum and a continuous sound field spectrum.
Description
The present invention relates to a risk situation that causes a sound field change based on a change pattern of correlation coefficients between sound field spectra according to multi-tone frequencies based on a sound field change with time. The present invention relates to a security monitoring device and a security monitoring method for detecting occurrence, detecting intrusion, motion, and fire and daily temperature change.
Security sensors used to independently detect intrusion and fire situations have been studied and used for a long time. Intrusion detection sensor may be any one of the passive infrared detection (PIR) method, IR method, ultrasonic method, sound detection method, vibration detection method, and microwave detection method, the fire detection sensor may be a temperature detection method, One of a smoke detection method, a gas detection method, and a flame detection method can be used.
However, as described above, these sensors are used as sensors for detecting one of intrusion and fire conditions. Security sensor that detects intrusion and fire as one sensor detects sound field change pattern measured using sound source with multitone frequency and uses the principle to extract and analyze characteristics according to time and frequency change As recently proposed.
As a conventional technology, there is a sound field security pattern technology that calculates an average and a deviation of a sound field, detects a dangerous situation of an intrusion, and informs an alarm based on a change value (SNR) of a sound field average value relative to an initial deviation of a reference sound field (Korean Application No. 2011) -0142499, Security system and method through analysis of sound field change pattern). However, this patent invention has a weak point that it takes time to set the reference value because at least two measurements must be performed at first to obtain the deviation of the reference sound field, and the method of detecting the randomness and the sound field change of the reference deviation obtained by a limited number of measurements. There is a weakness that the reliability of intrusion detection is weak due to inaccuracy. In addition, there was a weakness in this approach that made it difficult to distinguish between intrusion, fire and movement situations.
As another conventional technique, the temperature rise change such as fire is characterized by moving in a high frequency direction without changing the shape of the sound field pattern, and distinguishing the point that the shape of the sound field pattern itself changes in intrusion situations. There is a technique for distinguishing intrusion situation (Korean Application No. 2013-0122862, security surveillance system and security surveillance method). However, this patent invention is also unreliable due to the inaccuracy of the method of detecting the sound field change as the change of the average sound field value relative to the reference sound field deviation, and the method of quantifying the degree of change in the pattern form and the method of deriving the similar index through the difference index. There is a lot of randomness and the frequency shift index also has difficulty in making accurate quantitative discrimination because the result obtained is variable according to the situation. In addition, it was difficult to detect movements separately.
An object of the present invention is to detect the occurrence of a dangerous situation that causes a sound field change based on the change pattern of the correlation coefficient of the sound field spectrum according to the frequency of the multitone sound source based on the change of the sound field over time, intrusion and movement and The present invention provides a reliable security monitoring device and method for separately detecting daily temperature changes such as fire, crossover and heating and cooling.
SUMMARY OF THE INVENTION An object of the present invention is to provide a security monitoring apparatus and method based on pattern change detection of correlation coefficient of sound field spectrum which provides comprehensive security surveillance for detecting and distinguishing intrusion, movement, and fire situation and verifying image capture based on it. have.
According to an aspect of the present invention, there is provided a security monitoring method comprising: outputting a multitone sound wave composed of a linear sum of sine waves having a plurality of frequency components into a security monitoring space; Receiving and calculating a sound field from the multitone sound wave; Calculating and storing sound field information for each frequency through the sound field; Determining whether a sound field change occurs by comparing the reference sound field information for each frequency with the calculated sound field information; Analyzing whether the sound field change has been collected for a certain period of time, and classifying at least two or more of intrusion, motion, and temperature change situations based on the correlation between the reference sound field spectrum and the continuous sound field spectrum.
In an embodiment, the correlation is obtained by calculating a correlation coefficient value between the reference sound field spectrum and the continuous sound field spectrum.
In an embodiment, the correlation is obtained by using a correlation coefficient calculated by dividing the covariance value of the reference sound field spectrum and the continuous sound field spectrum by the product of standard deviations of the respective sound field spectrums.
In an embodiment,
Comparing the reference sound field spectrum with a current sound field spectrum to determine whether a dangerous situation causing the change in the sound field occurs; Analyzing the sound field spectrum collected for a certain period of time before the occurrence of the dangerous situation to classify the situation of intrusion, temperature change, or movement; And classifying a situation of intrusion, movement, fire, and temperature changes in daily work and heating and heating based on a correlation coefficient between the reference sound field spectrum and the continuous sound field spectrum.
In an embodiment, it is determined whether a dangerous situation occurs by comparing a correlation coefficient between the reference sound field spectrum and the current sound field spectrum with a set reference value.
In an exemplary embodiment, the correlation coefficient between the reference sound field spectrum and the current sound field spectrum may be used, and an initial correlation coefficient at the time of measuring the reference sound field may be a limit value of 1 and the current sound field and reference as an index indicating different degrees between sound field spectra. The correlation coefficient with the sound field is compared with a limit value of 1 to determine whether a dangerous situation occurs.
In an embodiment, using the change pattern of the correlation coefficient between the reference sound field spectrum and the continuous sound field spectrum, a sudden decrease immediately before the occurrence of the dangerous situation is determined as an intrusion, and a gradual decrease immediately before the occurrence of the dangerous situation is determined by temperature. It is determined by the change situation.
In an embodiment, the mean value of the correlation coefficient between the reference sound field spectrum and the continuous sound field spectrum before the occurrence of the dangerous condition is limited to 1, and the sound field spectrum and the reference sound field at the time of the occurrence of the dangerous condition are generated. By comparing the correlation coefficient values between the spectra and the ratio of the limit values from 1, the intrusion and the temperature change situation are distinguished.
In an exemplary embodiment, a temporal variation of a correlation coefficient between the reference sound field spectrum and the continuous sound field spectrum may be analyzed, and an irregularly increasing or decreasing change pattern may be determined as a movement, and a rapidly decreasing change pattern is invaded. It is determined by the situation.
The method may include determining whether a sound field change occurs by comparing the reference sound field spectrum with the current sound field spectrum; And when the sound field change occurs, analyzing the sound field spectrum for each frequency collected for a certain period of time before the occurrence of the sound field change situation to distinguish between human / animal movement, fire, and temperature change in daily work and heating and cooling.
The method may further include determining whether a sound field change situation occurs by comparing a correlation coefficient between the reference sound field spectrum and the current sound field spectrum with a set reference value.
In an embodiment, an aspect of temporal change in the correlation coefficient between the reference sound field spectrum and the continuous sound field spectrum may be analyzed, and the movement may be determined when the change is irregular, and the change may be gradually and gradually decreased. When the temperature change situation is determined.
In an embodiment, the absolute value of the difference between the before and after correlation coefficients obtained by successive measurements using the correlation coefficient between the reference sound field spectrum and the continuous sound field spectrum for a predetermined period before the occurrence of the sound field change situation is summed over the entire interval. The ratio between the value and the value obtained by subtracting the correlation coefficient value when the sound field change is detected from 1 is set as a motion index, and the motion and temperature change are distinguished using the motion index.
In an embodiment, an index indicating a frequency shift of a sound field spectrum is derived based on a correlation coefficient obtained by using a multitone frequency between the reference sound field spectrum and the continuous sound field spectrum as a variable, and the direction and duration of the frequency shift are derived. Considering the time taken, the situation of temperature change of fire, daily work crossing and air-conditioning is distinguished.
The method may include detecting and storing motion information of a person and an animal in a secure space using sound field information; And transmitting the sensed information to the guardian's smartphone and the smart device.
In an embodiment, the method may further include transmitting an alarm of falling, fainting and incapacitating accidents and transmitting security information when a movement of a person and an animal in the security space is not detected for a predetermined time.
In an embodiment, the method may further include selectively detecting only a fire situation, transmitting a fire alarm, and transmitting security information in a state where a human or animal moves inside the security space.
In an embodiment, the method may further include storing image information and performing image capturing to verify the situation when a security situation occurs.
In an embodiment, the security monitoring method is linked to a smart home appliance including a security camera having a network function, an Internet phone, a smart TV, an interphone.
In an embodiment, the interworking device is implemented in software without adding hardware of the companion device.
In an embodiment, when an app type program related to a user's smartphone or smart device is executed, the security monitoring method transmits security information that is executed or obtained by remote control.
Security monitoring apparatus according to an embodiment of the present invention, the sound generating device for outputting sound waves in accordance with the input voltage in the security monitoring space; A sound wave receiving device receiving the sound wave and calculating a sound field using the sound wave; And calculating the sound field spectrum information of the sound field through continuous measurement, calculating a cross correlation coefficient between the continuous sound field spectrum information and the reference sound field spectrum information, and inducing, moving, and temperature change situations through the cross correlation coefficient. And a sound field signal processing device for distinguishing at least two situations.
In an embodiment, the sound wave is a multitone sound wave consisting of a linear sum of sinusoids having a plurality of frequency components.
The memory device may further include a memory configured to store the reference sound field spectrum information.
In an embodiment, the sound field signal processing apparatus calculates a sound pressure or phase of the sound field using an acoustic transfer function.
FIG. 1 is a block diagram of a security monitoring device that detects and distinguishes daily changes in temperature such as intrusion and fire, and heating and cooling based on a correlation coefficient of a sound field spectrum.
FIG. 2 is a diagram illustrating a change in a sound field spectrum when a sudden intrusion occurs in a reference sound field and continuous sound field measurements using a multitone sound source having a center frequency of 4 kHz and a frequency interval of 4 Hz.
FIG. 3 shows the change in the cross-correlation coefficient obtained by varying the frequency shift between the reference sound field and each successive retrieval measurement value in the sound field spectrum obtained in FIG. 2 for each of two, six and ten times (after infiltration). Drawing.
FIG. 4 is a diagram showing a correlation coefficient without frequency shift (m = 0) between a reference sound field and each successive measured value in the sound field spectrum obtained in FIG. 2.
FIG. 5 is a diagram illustrating the maximum value of the cross correlation coefficient obtained for each frequency in consideration of the frequency shift between the reference sound field and each successive measured value in the sound field spectrum obtained in FIG. 2.
FIG. 6 is a diagram illustrating the frequency shift index corresponding to the maximum value of the correlation coefficient for each measurement frequency in order to indicate how much the frequency of each successive frequency spectrum is shifted compared to the reference sound field in the sound field spectrum of FIG. 2.
FIG. 7 is a view showing a gradual change in the sound field spectrum that occurs when a temperature change occurs due to a fire condition from an initial stage in a reference sound field and continuous sound field measurement using a multitone sound source having a center frequency of 4 kHz and a frequency interval of 4 Hz.
FIG. 8 is a diagram illustrating the change in the cross correlation coefficient obtained by varying the frequency shift between the reference sound field and each successive measured value in the sound field spectrum obtained in FIG. 7 as a variable for each of two, six, and ten times.
FIG. 9 is a diagram showing a correlation coefficient without frequency shift (m = 0) between a reference sound field and each successive measured value in the sound field spectrum obtained in FIG. 7.
FIG. 10 is a diagram illustrating the maximum value of the cross correlation coefficient obtained for each frequency in consideration of the frequency shift between the reference sound field and each successive measured value in the sound field spectrum obtained in FIG. 7.
FIG. 11 is a diagram illustrating the frequency shift index corresponding to the maximum value of the correlation coefficient for each measurement frequency in order to show how much the frequency of each successive frequency spectrum is shifted compared to the reference sound field in the sound field spectrum of FIG. 7.
FIG. 12 is a diagram showing the change in the sound field spectrum which appears when the intrusion situation suddenly occurs (15 times) in the reference sound field and continuous sound field measurement using a multitone sound source having a center frequency of 6 kHz and a frequency interval of 4 Hz.
FIG. 13 is a graph showing the change in the cross correlation coefficient obtained by the frequency shift between the reference sound field and each successive measured value in the sound field spectrum obtained in FIG. 12 as a variable for each of three, nine and fifteen times.
FIG. 14 is a diagram showing a change in a correlation coefficient without frequency shift (m = 0) between a reference sound field and each successive measured value in the sound field spectrum obtained in FIG. 12.
FIG. 15 is a diagram illustrating the maximum value of the cross correlation coefficient for each frequency in consideration of the frequency shift between the reference sound field and each successive measured value in the sound field spectrum obtained in FIG. 12.
FIG. 16 is a diagram illustrating the frequency shift index corresponding to the maximum value of the correlation coefficient for each measurement frequency in the monitoring mode in order to show how much the frequency of each successive frequency spectrum is shifted compared to the reference sound field in the sound field spectrum of FIG. 12. to be.
FIG. 17 is a diagram showing a gradual change in the sound field spectrum that occurs when a fire situation occurs from the beginning in a reference sound field and continuous monitoring mode sound field measurement using a multitone sound source having a center frequency of 6 kHz and a frequency interval of 4 Hz.
FIG. 18 is a diagram showing the change in the cross correlation coefficient obtained by the frequency shift between the reference sound field and each successive measured value in the sound field spectrum obtained in FIG. 17 for each of three, nine and fifteen times.
FIG. 19 is a diagram illustrating a change in a correlation coefficient without frequency shift (m = 0) between a reference sound field and each successive measured value in the sound field spectrum obtained in FIG. 17.
20 is a diagram illustrating the maximum value of the cross correlation coefficient for each frequency in consideration of the frequency shift obtained between the reference sound field and each successive measured value in the sound field spectrum obtained in FIG. 17.
FIG. 21 shows the frequency shift index corresponding to the maximum value of the cross correlation coefficient for each measurement frequency in the monitoring mode in order to show how much the frequency of each successive frequency spectrum is shifted compared to the reference sound field in the sound field spectrum of FIG. 17. Drawing.
FIG. 22 is a flow chart illustrating security monitoring by detecting intrusion, fire, and daily temperature change based on a correlation coefficient of a sound field spectrum.
FIG. 23 is a block diagram of a risk situation monitoring apparatus for detecting an accident of an elderly person or a pet living alone based on a correlation coefficient of a sound field spectrum from a fire and a daily temperature change situation.
FIG. 24 is a flowchart illustrating a risk situation monitoring for detecting an accident of an elderly person or a pet living alone based on a correlation coefficient of a sound field spectrum from a fire and a daily temperature change situation.
FIG. 25 is a diagram showing the change in the sound field spectrum when a human is continuously moving in a reference sound field and continuous sound field measurements using a multitone sound source having a center frequency of 4 kHz and a frequency interval of 4 Hz.
FIG. 26 is a diagram illustrating a correlation coefficient without frequency shift (m = 0) between a reference sound field and each successive measured value in the sound field spectrum obtained in FIG. 25.
FIG. 27 is a view showing a comparison of characteristics in which a correlation coefficient without frequency shift (m = 0) changes between a reference sound field and each successive measured value due to temperature change, invasion, and movement.
FIG. 28 is a flow chart illustrating security surveillance by detecting intrusion, movement, fire, and daily temperature change based on a correlation coefficient of a sound field spectrum.
Hereinafter, exemplary embodiments of the present invention will be described with reference to the accompanying drawings so that those skilled in the art may easily implement the technical idea of the present invention.
FIG. 1 is a block diagram of a
The
The sound pressure of the
The sound receiving apparatus 120 may receive the sound waves in the security surveillance space and obtain sound pressure from the received sound waves. Here, the sound receiving apparatus 120 may include a frequency conversion filter for converting the received sound wave into the frequency domain.
The sound field
Accordingly, the sound field
Then, the sound field
More specifically, the sound field
In this case, the sound field
Similarly, as the speed of sound waves changes due to air temperature change when a fire occurs in the security surveillance space, a change in the sound field occurs, and an acoustic receiver installed inside the security surveillance space changes the sound field of the sound waves according to the temperature distribution. It can be detected differently.
Referring back to FIG. 1, when the intrusion situation occurs in the security surveillance space, the boundary condition is changed, so the acoustic transfer function is changed, and thus the sound field is changed. It can happen better within. In this way, by detecting a change in the sound field, it is possible to detect a blind spot in which no intrusion in the blind spot or a flame or smoke is observed. On the other hand, there may be a misunderstanding of a dangerous situation due to sound field changes caused by heating and cooling or crossover. Procedures and processes are needed to distinguish these situations.
The existing security monitoring method quantifies the degree of change based on the SNR (signal to noise ratio) versus the deviation of the reference sound field, which is obtained by a number of measurements at the time of measuring the sound field change. This method takes time for measuring the noise of the initial sound field, and also leads to inaccuracy due to a limited number of reference deviations. In addition, in the case where the deviation of the initial sound field is zero, a problem in which an error occurs in the calculation should be considered. Furthermore, in the case of obtaining a sound field change by a change value of the mean value relative to the reference deviation for each frequency, when the sound pressure at the frequency of the destructive interference is very low, there is a limit that the error is large because the reference deviation is relatively large.
In contrast, the security monitoring method according to an exemplary embodiment of the present invention calculates a correlation coefficient for accurately deriving a similarity degree between the reference sound field spectrum and the changed current sound field spectrum, and quantifies the degree of change of the sound field based on the correlation coefficient. Therefore, the reliability can be improved in detecting and distinguishing a dangerous situation.
FIG. 2 is a diagram illustrating a change in a sound field spectrum when a sudden intrusion situation occurs in a reference sound field and continuous sound field measurements using a multitone sound source having a central frequency of 4 kHz and a frequency interval of 4 Hz. Referring to FIG. 2, after the reference sound field spectrum is measured in the security surveillance space, an experimental result obtained by continuously measuring 10 sound field spectrums with a time interval of 8 seconds is shown. From 1 to 9 times, the sound field spectrum before intrusion is obtained, and in 10 times, it is the sound field spectrum obtained after the intrusion situation occurs.
The multitone sound source used for sound field measurement has a center frequency of 4 kHz, a frequency interval of 4 Hz, and all have a frequency of 17 channels. The
FIG. 3 is a diagram showing a cross correlation coefficient obtained by using a frequency shift between a reference sound field spectrum and each of the sound field spectra measured twice, six times, and ten times as a variable. The cross correlation coefficient can be expressed by the following equation.
In
Specifically, when m = 0, the cross correlation coefficient is the result of dividing the covariance of two sound field spectra which are not frequency shifted by the product of the standard deviation values of the respective sound field spectra measured in the i th and j th measurements. to be. In addition, when m is not 0, the correlation coefficient between the i-th sound field spectrum and the j-th sound field spectrum shifted by m is calculated.
Referring to FIG. 3, the cross correlation coefficients R 0 , 2 (m) and R 0 , 6 (m) between the reference sound field spectrum and the sound field spectrum before intrusion (twice and six times) are almost similar. However, the cross correlation coefficient R 0 , 10 (m) between the reference sound field spectrum and the post-intrusion (10 times) sound field spectrum varies greatly.
FIG. 4 is a diagram showing a correlation coefficient without frequency shift (m = 0) between a reference sound field and each successive measured value in the sound field spectrum obtained in FIG. 2. Referring to FIG. 4, the correlation coefficient when m = 0 in
FIG. 5 is a diagram illustrating the maximum value of the cross correlation coefficient obtained for each frequency in consideration of the frequency shift between the reference sound field and each successive measured value in the sound field spectrum obtained in FIG. 2. Referring to FIG. 5, the maximum value of the cross correlation coefficient with the reference sound field spectrum is displayed for each measurement number in consideration of the case where m is not 0, that is, all cases of frequency shift in
FIG. 6 is a diagram illustrating the frequency shift index corresponding to the maximum value of the correlation coefficient for each measurement frequency in order to indicate how much the frequency of each successive frequency spectrum is shifted compared to the reference sound field in the sound field spectrum of FIG. 2. Referring to FIG. 6, when m is 0 as shown in FIG. 5, since all of the correlation coefficients are maximum values, the frequency shift index is all zero.
FIG. 7 is a view showing a gradual change in the sound field spectrum that occurs when a temperature change occurs due to a fire condition from an initial stage in a reference sound field and continuous sound field measurement using a multitone sound source having a center frequency of 4 kHz and a frequency interval of 4 Hz. Referring to FIG. 7, after the reference sound field spectrum is measured in the security monitoring space, an artificial fire situation is created by using an electric heater, and the sound field spectrum is continuously measured 10 times with a time interval of 8 seconds. As shown in FIG. 7, the sound field spectrum is gradually shifted in the high frequency direction.
In general, the speed (v) of the sound wave can be expressed as
Therefore, even if the frequency f of the sound waves is the same, the wavelength l has a property of increasing in proportion to the temperature T of the air by the following equations (3) and (4).
As the temperature of the air inside the security space rises, the speed of sound waves increases. As a result, the wavelength increases proportionally at the same frequency. Since the size inside the secure space is fixed, the wavelength of the sound wave must be constant in order for the sound receiving apparatus at the same position to have the same sound pressure when the temperature increases. As a result, the sound pressure level pattern moves in the high frequency direction without changing its shape. In this case, the change value δf of the moving frequency may be simply expressed as in
Since the speed change δv of the sound wave is proportional to the temperature change δT in
The change in temperature of the air due to the actual fire is difficult to simplify with the increase of the overall temperature. Local and global temperature changes around the fire are complex. However, the degree to which the sound pressure level pattern moves at a high frequency due to the temperature rise can generally monitor the change in the internal air temperature. For example, when the center frequency of the multitone frequency is 4 kHz and the frequency gap is 4 Hz, the temperature change amount δT is represented by
FIG. 8 is a diagram showing a cross correlation coefficient obtained by using a frequency shift between a reference sound field spectrum and each sound field spectrum measured continuously, twice, six times, and ten times as a variable. Referring to FIG. 8, the cross correlation coefficient is gradually changed compared to the case of intrusion shown in FIG. 3. In the case of (c), the maximum value of the cross-correlation coefficient between the reference sound field spectrum and 10 measurements is shown at m = 1. This is in agreement with the result of the sound field spectrum shown in FIG. 7 shifted by about 4 Hz at high frequency.
FIG. 9 is a diagram illustrating a change in a correlation coefficient according to the number of measurements when there is no frequency shift with m = 0 as shown in FIG. 4. Referring to FIG. 9, the correlation coefficient gradually decreases to about 1 as compared to the situation of intrusion shown in FIG. 4. This means that the sound field spectrum gradually shifted to high frequency with increasing temperature.
FIG. 10 is a diagram illustrating the maximum value of the cross correlation coefficient obtained for each frequency in consideration of the frequency shift between the reference sound field and each successive measured value in the sound field spectrum obtained in FIG. 7. Considering the case where m = 0 is not shown in FIG. 5, that is, all cases of frequency shift. The cross correlation coefficient becomes the maximum value when the 4 Hz high frequency shift is carried out in the ten measurements shown in FIG. Therefore, although the result of FIG. 9 and FIG. 10 differs in the correlation coefficient in ten times, the difference is not large.
FIG. 11 is a diagram illustrating the frequency shift index corresponding to the maximum value of the correlation coefficient for each measurement frequency in order to show how much the frequency of each successive frequency spectrum is shifted compared to the reference sound field in the sound field spectrum of FIG. 7. Referring to FIG. 11, the frequency shift value at which the cross correlation coefficient is the maximum value is shifted at 4 Hz high frequency only when measured 10 times.
12 to 21, the reference sound field spectrum was measured using 6 kHz, which has a higher center frequency in the security surveillance space, followed by measuring 15 sound field spectra successively with a time interval of 8 seconds. The figures are about the results. In this case, the frequency interval is 4 Hz and all have 17 channels of frequencies, and the
FIG. 12 is a diagram showing the change in the sound field spectrum which appears when the intrusion situation suddenly occurs (15 times) in the reference sound field and continuous sound field measurement using a multitone sound source having a center frequency of 6 kHz and a frequency interval of 4 Hz. Referring to FIG. 12, it can be seen that the sound field spectrum rapidly changes in 15 measurements in which the intrusion situation occurs.
FIG. 13 is a graph showing the change in the cross correlation coefficient obtained by the frequency shift between the reference sound field and each successive measured value in the sound field spectrum obtained in FIG. 12 as a variable for each of three, nine and fifteen times. Referring to FIG. 13, a sudden change occurs in the 15 times in which an intrusion situation occurs in representing a reference sound field spectrum and a cross correlation coefficient between 3 times, 9 times, and 15 times.
FIG. 14 is a diagram showing a change in a correlation coefficient without frequency shift (m = 0) between a reference sound field and each successive measured value in the sound field spectrum obtained in FIG. 12. Referring to FIG. 14, the correlation coefficient in the case of m = 0 without a frequency shift was nearly changed to 1 before invasion, but was rapidly decreased at 15 times when the invasion situation occurred. This indicates that the sound field spectrum when an invasion situation is completely different compared to the reference sound field spectrum.
FIG. 15 is a diagram illustrating the maximum value of the cross correlation coefficient for each frequency in consideration of the frequency shift between the reference sound field and each successive measured value in the sound field spectrum obtained in FIG. 12. Referring to FIG. 15, the maximum value of the cross correlation coefficient obtained by considering all cases where m is not 0 in consideration of frequency shift is a value between 0.5 and 0.6.
FIG. 16 is a diagram illustrating the frequency shift index corresponding to the maximum value of the correlation coefficient for each measurement frequency in the monitoring mode in order to show how much the frequency of each successive frequency spectrum is shifted compared to the reference sound field in the sound field spectrum of FIG. 12. to be. Referring to FIG. 16, the frequency shift index (m) corresponding thereto was 6, but the frequency shift index does not indicate that the actual sound field spectrum shifts because the cross correlation coefficient is not large even when frequency shift is considered. This is because the frequency shift index does not have much meaning unlike a fire in which the sound field spectrum shifts at a high frequency.
FIG. 17 is a diagram showing a gradual change in the sound field spectrum that occurs when a fire situation occurs from the beginning in a reference sound field and continuous monitoring mode sound field measurement using a multitone sound source having a center frequency of 6 kHz and a frequency interval of 4 Hz. Referring to FIG. 17, when the fire situation occurs from one time, it can be seen that the sound field spectrum gradually moves to high frequency from 1 to 15 times.
FIG. 18 is a diagram showing the change in the cross correlation coefficient obtained by the frequency shift between the reference sound field and each successive measured value in the sound field spectrum obtained in FIG. 17 for each of three, nine and fifteen times. Referring to FIG. 18, the cross-correlation coefficient between the reference sound field spectrum and 3 times, 9 times, and 15 times shows that the frequency shift index corresponding to the maximum value gradually increases.
FIG. 19 is a diagram illustrating a change in a correlation coefficient without frequency shift (m = 0) between a reference sound field and each successive measured value in the sound field spectrum obtained in FIG. 17. Referring to FIG. 19, it can be seen that the correlation coefficient in the case of m = 0 without a frequency shift is close to 1 at the beginning of the fire situation but gradually decreases with time and decreases between 0.1 and 0.2 after 15 times.
20 is a diagram illustrating the maximum value of the cross correlation coefficient for each frequency in consideration of the frequency shift obtained between the reference sound field and each successive measured value in the sound field spectrum obtained in FIG. 17. Referring to FIG. 20, even when m is not 0 in consideration of frequency shift, the maximum value obtained periodically varies between 0.8 and 1.
FIG. 21 shows the frequency shift index corresponding to the maximum value of the cross correlation coefficient for each measurement frequency in the monitoring mode in order to show how much the frequency of each successive frequency spectrum is shifted compared to the reference sound field in the sound field spectrum of FIG. 17. Drawing. Referring to FIG. 21, it can be seen that the frequency shift index m gradually increases as 0 to 1 to 6 times, 1 to 7 to 12 times, and 2 to 13 to 15 times. Since the cross-correlation coefficient is close to 1 in consideration of the frequency shift, the frequency shift index indicates that the actual sound field spectrum shifts at a high frequency, so that there is a reliable change in temperature. As the center frequency increases, so does the frequency shift. The security monitoring method using the change pattern of the correlation coefficient between the sound field spectrum implemented through the sound field measurement is as follows.
The security monitoring method according to an embodiment of the present invention calculates a cross-correlation coefficient between a reference sound field and a predetermined number of consecutive sound fields in a sound field spectrum obtained through continuous measurement, and calculates a cross correlation coefficient between m = 0 without frequency shift. The correlation coefficient is monitored to determine that a security situation such as an intrusion or fire has occurred when the value is smaller than a predetermined reference value smaller than one. Correlation coefficients are criteria for quantifying correlations that determine how similar two spectra are. In the case of the discrimination reference value, it may be set to a constant value of 1 or less according to the environment or conditions. In an embodiment of the present invention, the determination reference value may be set to about 0.95, but the present invention is not limited thereto.
In another embodiment, the security monitoring method does not use correlation coefficient values indicating similar levels between sound field spectra, but calculates a value obtained by subtracting the correlation coefficient from 1 as an index indicating different degrees, and continuously comparing the reference sound field with the reference sound field. The occurrence of a dangerous situation can be determined by comparing two values obtained in the current sound field. Using the correlation coefficient obtained by measuring a constant number of sound fields at the beginning, a method of determining a risk situation as a ratio of the limiting value of the correlation coefficient at 1 and the limiting value of the correlation coefficient between the real-time sound field and the initial sound field at 1 may be used. . Similar to the sound field change detection method using the standard deviation and signal value of the sound field, the risk situation is compared by comparing the average and the deviation of the index representing the different degree of the initial measurement and the index representing the different degree in the real-time sound field measurement. The method of discrimination can be applied. Compared with the conventional method, this method can greatly improve its reliability and sensitivity.
Security monitoring method according to another embodiment of the present invention, the sound field value may have the wrong data instantaneously due to external noise or electrical noise of the acoustic device, thereby minimizing the reliability problem of the sound field security that may occur and reliability In order to increase, the step of repeating the sound field measurement or increasing the size of the sound source may re-measure the sound transfer function to check the sound field change caused by the occurrence of the dangerous situation. The correlation coefficient uses the relative deviation from the average regardless of the absolute size of the sound field, so when the sound source is very large compared to the noise of the surrounding environment, the correlation coefficient has the same value regardless of the size of the sound source. Therefore, the same result can be obtained regardless of the size change of the sound source even if the sound pressure level of the sound receiving element itself is used as it is, instead of the sound transfer function considering the applied voltage of the sound generating element.
The sound field spectrum can be sensitively changed not only by intrusion and fire, but also by changes in daily temperature environment such as daily crossing and heating and cooling. Therefore, it is difficult to accurately distinguish a security situation by simply measuring a correlation coefficient without considering frequency shift. First of all, in order to solve this problem, it is necessary to distinguish between a situation in which the sound field changes very rapidly such as intrusion and a situation in which the sound field gradually changes, such as temperature changes such as fire, crossover and air conditioning. In case of intrusion, as shown in FIG. 4, the value of the correlation coefficient is maintained before invasion, and when the intrusion occurs, the correlation coefficient decreases very rapidly, and in case of fire and daily temperature change, as shown in FIG. 9. Likewise, the correlation coefficient gradually decreases.
The security monitoring method according to the embodiment of the present invention, in order to distinguish the pattern of the change in the correlation coefficient according to the time change, the correlation of the predetermined number of predetermined intervals before the time when the correlation coefficient becomes smaller than the reference value due to intrusion or temperature change Comparing the mean value of the coefficient (R a ) with the value of the correlation coefficient (R b ) at the time of intrusion or fire hazard.
This ratio indicates how many times the difference between the reference sound field and the sound field at the time of occurrence of the security situation is compared with the average difference between the reference sound field and the average sound field in the interval before the security situation occurs. As a result, the V value of
For example, since R a is 0.9995 and R b is 0.9103, which is a case of intrusion, an index (V) value representing a degree of abrupt change of correlation coefficient is 193.37, and is shown in FIG. 9 which is a case of temperature change. Since R a is 0.9774 and R b is 0.9247, the V value corresponds to 3.33, resulting in a very large difference. Therefore, by setting a predetermined reference value, the situation of intrusion and temperature change can be distinguished.
However, in everyday environments, intrusions and temperature changes can be mixed, and slight intrusions or movements and temperature changes can occur at the same time that sound field changes can't be detected within a specified measurement interval. In some cases, these two situations can occur sequentially, so that a change in sound field can be detected. When the sound field change is detected by the movement of a slight object that is finally considered to be an intrusion in the state of temperature change, the condition that the change value of the correlation coefficient due to the intrusion is larger than the specific gravity of the change value of the correlation coefficient due to the fire is approximately. 6 or so. Thus, in general, it can be classified as a fire if V is less than 6, and an intrusion if V is greater than 6. However, the present invention is not limited to this value. In the intrusion situation of FIG. 14 and the temperature change situation of FIG. 19, V becomes 376.61 and 2.63, respectively.
If a sound field change is detected even if there is a slight intrusion or movement of the object, but the sound field change is not detected, it is a change that does not occur more than a predetermined reference value inside the security space. In the case of the exponent V indicating the sound field change, it is lower than 6. In this case, even if the intrusion alarm does not sound because it is classified as a temperature change, there is no big problem in the reliability of the security detection system.
Security monitoring method according to an embodiment of the present invention, when the sound field value may have wrong data instantaneously due to external noise or electrical noise of the acoustic device, and also when the sound field change is detected due to temperature change and slight intrusion situation In order to minimize the problem of sound field security and increase the reliability of the sound field, it may be necessary to repeat the sound field measurement or increase the size of the sound source to re-measure the sound transfer function to reconfirm the sound field change caused by the occurrence of a dangerous situation. Can be. This improves the accuracy of intrusion detection and accurately measures the reference sound field after confirming that it is a safe situation.
When the security monitoring method according to an embodiment of the present invention is determined to be an intrusion situation, it is possible to cope with a dangerous situation by transmitting an alarm indicating an intrusion situation and taking, storing and transmitting an image of a security space. However, if it is determined that the temperature change situation, it is necessary to distinguish between the daily temperature change situation, such as fire and day crossing and air-conditioning, so that subsequent processes are required. In this case, the temperature change situation can be determined by distinguishing whether the frequency shift of the sound field spectrum moves at high frequency or low frequency and whether the frequency shift is continuously occurring.
In the case of the temperature rise, the frequency shift index moves in the high frequency direction as shown in FIGS. 11 and 21. In the case of a temperature drop, the frequency shift index moves in the low frequency direction. In addition, if the temperature rise continues through a repeating measurement of a certain period, it is monitored in the subsequent repeated measurement whether or not this phenomenon persists, and whether it is a normal or temporary temperature rise by heating or crossover or a continuous temperature rise by fire Can be distinguished.
As described above, the security monitoring method based on the sound field change pattern detection using the correlation coefficient of the sound field spectrum according to an embodiment of the present invention, by detecting the dangerous situation of security at the beginning of the intrusion situation or the fire situation, In addition, an alarm can be issued depending on the fire situation. In addition, when interlocked with a camera module such as a CCTV, the security surveillance system may store or transmit captured images related to an intrusion or fire situation to a set destination. Here, the destination may be a car wireless remote device of a specific person, smart devices such as smartphones and tablet PCs, security office server, security company server, fire department server, police station server, and the like.
FIG. 22 is a security monitoring flowchart for separately detecting an intrusion, a fire, and a daily temperature change based on a correlation coefficient of a sound field spectrum according to an exemplary embodiment of the present invention.
FIG. 22 may be implemented by the operation of the sound field
The preparation mode may include an initial setting step (S2200), an hourly sound field spectrum measurement step (S2210), an hourly sound field spectrum analysis step (S2220), and a security monitoring condition setting step (S2230).
In the monitoring mode, a sound field spectrum change measurement step (S2300), a risk situation suspect determination step (S2310), an intrusion / temperature change classification step (S2320) through the correlation coefficient analysis, an intrusion situation determination step ( S2330), intrusion image acquisition step (S2340), intrusion alarm issuance and information transmission step (S2350), fire / routine temperature change classification step (S2360) analyzing the frequency shift index, fire situation determination step (S2370), fire image acquisition A step S2380 and a fire alarm initiation and alarm delivery step S2390 may be included.
In the initial setting step (S2200), the
In the sound field spectrum measurement step S2210, the sound field
In the hourly sound field spectrum analysis step (S2220), the sound field
In the security monitoring condition setting step (S2230), the sound field
In the sound field change measurement step (S2300) under the security monitoring mode, the sound field
As another example, a method of setting a measurement sound field before a predetermined section as a reference sound field and moving the reference sound field back one by one every time the current real-time sound field is measured may be used. The advantage of this method is that it is possible to compare and measure the change in the sound field at the same period compared to the standard before the predetermined number of intervals. In addition, the measured initial sound field is fixed to the reference sound field until the measurement of the predetermined number of sections is completed, and the corresponding sound field is extended to the virtual preceding sound field within the corresponding section, or the predetermined number of section measurements are all If a sound field change occurs before it has elapsed, an unconditional situation may be chosen. In general, this interval may be set as an initialization period.
In a step S2310 of determining a dangerous situation such as a fire or an intrusion, the sound field
In the correlation coefficient analysis step S2320 of the sound field spectrum, when it is determined that a dangerous situation has occurred, the sound field
If it is determined that the intrusion in the intrusion situation determination step (S2330), the intrusion image acquisition step (S2340), in order to accurately verify, the camera module is operated by the control of the sound field
In the intrusion alert command and information delivery step (S2350), the sound field
When it is determined as the temperature change situation in the intrusion determination step (S2330) and proceeds to the frequency shift analysis step (S2360), the frequency shift index is derived by analyzing the change in the cross correlation coefficient according to the frequency shift. If the situation moves in the low frequency direction, the sound field change measurement step (S2300) is performed again as a steady state by determining the effect of cooling. However, if the situation is to move in the high frequency direction from the frequency shift index, since the fire is a suspected situation on the basis of how long such a situation is determined in the fire suspect step (S2370) whether the fire situation occurs.
In a specific embodiment, the sound field change measurement step (S2300) to intrusion situation determination step (S2330) and the frequency shift analysis step (S2360) to the fire suspect step (S2370) by repeating the level two times or more continuously higher than the reference value When a rise in the temperature is detected, a fire situation can be determined.
If it is determined that the fire situation in the fire situation determination step (S2370), in the fire image acquisition step (S2380), to accurately verify this, the camera module is operated by the control of the sound field
In the fire alarm command and information delivery step (S2390), the sound field
In an exemplary embodiment, when the analysis section unit of the sound field spectrum is set to two or three short periods in the correlation coefficient analysis step S2320 of the sound field spectrum, a rapidly changing sound field spectrum may be selectively detected. Most intrusion situations can be detected separately from relatively slow temperature change by comparing the change of correlation coefficient without using the sudden change index of
In this case, however, the detailed change of the sound field spectrum due to temperature change cannot be detected. Therefore, by comparing the sound field spectral change detection results according to the two section conditions by performing the calculation process by setting the analysis section unit of the sound field spectrum to a relatively long section, and comparing the mutually invasive or detailed temperature change situation accordingly. The form of the method may be used. In this case, the sensitivity of the intrusion or the fire detection may be adjusted by differentiating the reference value of the change of the correlation coefficient from the intrusion or the fire in the short term or the long term. In addition, when it is classified as a temperature change situation, a dangerous situation may be distinguished by using a process of distinguishing the temperature rise or fall to analyze the frequency shift index.
As various usage examples, the security monitoring apparatus to which the security monitoring method is applied may be connected to an Internet telephone and used as an integrated or external type. The security monitoring method may be applied to various types of smart devices such as smart phones, smart TVs, smart cars, safes or interphones.
One or more modules with a security surveillance function can be installed inside a home, office, store, factory, warehouse, etc., which are set as security spaces, and can operate independently or can be connected to each other via wired or wireless connection. Basically, a pair of sound generating devices and sound receiving devices are configured as a sensing module so that sound field signal processing is integrated, but it is difficult for reliable sound field security monitoring because the security space is too wide or the structure is complicated. There is also a system configuration in which a plurality of sound generating device and sound sensing device pairs are connected by wire, or a short range communication module such as WiFi, centering on a system operating as a sound field signal processing device.
Multitone sound sources with frequencies above 15 kHz that are inaudible to humans by operating in the audible frequency range while all people are out and setting doors and windows as critical security spaces when the person is limited to a designated indoor space or when sleeping. By selectively operating the noise problem can be solved. In the audible frequency range of 20 ~ 15k Hz, the wavelength of sound waves is large, so there is no blind spot due to the structure inside the security space, so a wide range of security monitoring is possible. Is possible.
Security monitoring method according to an embodiment of the present invention, in order to monitor the movement of the elderly, pets, etc. living alone in the house to detect and store the sound field information in real time, or when there is no movement for a long time fall, fainting and impairment It can also provide a way to send a risk alert to a guardian's smartphone. In this case, as a sound source, a hearing loss and inaudible frequency range of 15 kHz or more that humans or animals cannot hear well can be used. It can be implemented in the form of monitoring that the detection of movement of the elderly or pets living alone for a certain time.
In this case, it is necessary to distinguish the fire situation by ignoring the intrusion situation or the daily temperature change, and operate as a procedure to issue a fall, faint and incapacitated alarm when the motion is not detected for a long time. This is possible. 23 and 24 are conceptual diagrams and flowcharts showing these functions.
FIG. 23 is a diagram illustrating that the sound transmission function changes and the sound field changes according to the change in the boundary condition due to the movement of the elderly or pets living alone in the security surveillance space. Similarly, the sound field changes as the speed of sound waves changes due to the occurrence of fire, air-conditioning or air temperature change in the security monitoring space. If there is no movement for a certain period of time, there is a risk of falling, fainting and incapacitating accidents, so it is necessary to detect this and detect the fire situation, but due to the sound field changes caused by heating and heating Mistakes can occur, so procedures and procedures are needed to distinguish these situations.
FIG. 24 is a flowchart illustrating a risk situation monitoring for detecting an accident of an elderly person or a pet living alone based on a correlation coefficient of a sound field spectrum from a fire and a daily temperature change situation. Referring to FIG. 24, a security monitoring method according to an embodiment of the present invention is largely divided into a preparation mode and a monitoring mode.
The preparation mode may include an initial setting step (S2400), an hourly sound field spectrum measurement step (S2410), an hourly sound field spectrum analysis step (S2420), and a security monitoring condition setting step (S2430).
The monitoring mode includes a sound field spectrum change measurement step (S2500), a sound field change determination step (S2510), a motion / temperature change classification step (S2520) through a correlation coefficient analysis, and a motion situation determination step (S2530) to measure a change in the correlation coefficient. In the fire / daily temperature change classification step (S2540) analyzing the frequency shift index, the fire situation determination step (S2550), the fire image acquisition step (S2560), the fire alarm issuing and alarm transmission step (S2570), in the sound field change determination step Incident suspicion step (2580), the accident image acquisition step (S2590), and the accident alert and information transmission (S2600) for determining that the movement does not occur for a predetermined time or more.
From the initial setting step (S2400) of the security monitoring preparation mode to the sound field change measurement step (S2500) of the security monitoring mode is the same as that of FIG. 22, but the use of the hearing loss or inaudible frequencies of 15 kHz or more is possible. It is different. In addition, in FIG. 22, the intrusion is suddenly monitored in the absence of motion, but in the case of FIG. 24, the monitoring of the continuous motion is different.
In the sound field change determination step (S2510), the sound field
When it is determined in step S2520 that the sound field change has occurred, the sound field
As shown in FIG. 22, a method of setting a measurement sound field before a predetermined number of intervals as a reference sound field as another embodiment and moving the reference sound field sequentially every time the current sound field is measured may be used. In this case, a section for analyzing the number of initialization cycles may be set.
FIG. 25 is a diagram illustrating an experimental result obtained by continuously measuring ten sound field spectra at a time interval of 8 seconds after measuring a reference sound field spectrum in a security surveillance space. Referring to FIG. 25, it is a sound field spectrum obtained when a mannequin assuming a person from one to ten times moves slowly. The multitone sound source used for the sound field measurement has a center frequency of 4 kHz, a frequency interval of 4 Hz, and a total of 17 channels, and the
It is a figure which shows the correlation coefficient in the case of m = 0 between a reference sound field spectrum and the sound field spectrum of each measurement frequency. Referring to FIG. 26, it can be seen from the initial measurement that the correlation coefficient sharply drops to 0 or less, and a large increase and decrease is irregularly large according to the number of measurements. The change in the correlation coefficient around zero means that the sound field spectra are completely different from each other. It may also include dynamic characteristics according to the movement of a person. In the actual security setting situation, the sound field change is detected from the initial measurement, and all the sound field spectrums stored in the predetermined interval should be considered and used in the analysis.
FIG. 27 is a diagram illustrating a representative form of a correlation coefficient (m = 0 without frequency shift) between a reference sound field spectrum and a sound field spectrum of a predetermined section up to the time when a sound field change situation occurs. In general, the number of times (M) can be set to the number of initialization cycles of the reference sound field spectrum, and the A graph shown by the dotted line shows the form of a gradual temperature change, and the B graph shows the correlation coefficient due to the presence of both temperature change and invasion. Show the form of change. And the C graph is a typical form of intrusion, while the D and E graphs are in the form of correlation coefficients in a continuous human movement.
The absolute value of the difference between the correlation coefficients obtained from successive measurements is summed over the total interval, and this value is calculated by dividing the value of the motion index by dividing the correlation coefficient value (R b ) by the limit value when the sound field change is detected by 1 Shown in In A, B, and C, which are graphs of rapid temperature changes and intrusion conditions, only the decrease of the correlation coefficient continuously occurs, so the value of this motion index is close to one. However, since the correlation coefficients increase and decrease in D and E, which are graphs of continuous movement, the motion index value is much larger than 1.
Therefore, by calculating the motion index value MOVE obtained in
Since intrusion is not considered, this method can distinguish between motion and temperature changes. However, the present invention is not limited to this method, and various types of methods may be applied.
In fact, the motion index value MOVE obtained using
When the movement situation is determined in the movement state determination step (S2530), since the elderly or pets living alone are still in the normal situation, the process returns to the sound field change measurement step (S2500). Since there may be a situation in which the sound field is temporarily changed by external noise or electrical noise of the device, as another embodiment of the present invention, the process of checking the sound field change by measuring the sound field again using the same conditions or a larger sound pressure may be performed. Can be added.
If the movement is not detected, this is a sound field change situation due to temperature change, so that the frequency shift analysis step (S2540) distinguishes the fire from the daily temperature change, and if the fire is certain in the fire suspect step (S2550), the sound field signal processing device 130 ) Acquires an image in the fire image step (S2560), issues a fire alarm, and transmits information (S2570). Images taken through the camera module may be transmitted to a server such as a mobile phone, a smart device, a security office, or a fire station through a wired or wireless communication network.
If the sound field change is not detected in the sound field change determination step (S2510), it is determined whether no movement is detected during the time interval set in the suspected accident step (S2580) to determine the occurrence of an accident of a fall, fainting or inactivity. The sound field
In an embodiment of the present invention, in the security monitoring concept and flow chart that detects the intrusion and fire and the daily temperature change of Figs. 1 and 22, the intruder moves slowly in the security space or there is a minute movement in the security space. In the case of intrusion at, the change in the correlation coefficient of the sound field spectrum is not abrupt, and when it is not distinguished from the aspect of the temperature change and the intrusion situation is not detected, the temperature as shown in FIG. 27 using the motion detection index of
For example,
FIG. 28 is a flowchart illustrating a security monitoring for detecting an intrusion, a fire, and a change in daily temperature as well as movement in an embodiment of the present invention. Although almost the same as in FIG. 22, a step of analyzing a motion index (S2360) and a step of distinguishing a motion and a temperature change based on this are added (S2370). Accordingly, if it is determined to be a movement, it is regarded as an intrusion, and the process proceeds to the step of acquiring an intrusion image and issuing an alarm, and if it is determined to be a temperature change, if it is determined to be a fire through the step of distinguishing between a fire and a daily temperature change (S2390). An alarm is issued.
In addition, it is possible to implement a fire safety monitoring function that selectively detects only a fire situation while ignoring people's movements in a space where people are active. The scope of the invention includes all such various forms of variation and modification.
In the embodiment of the present invention, a security monitoring method for classifying risk situations by analyzing the time-varying aspect of the correlation coefficient of the sound field spectrum, but analyzing the change of the correlation coefficient for each center frequency before and after the occurrence of the risk situation Therefore, it can be applied to security monitoring method that distinguishes temperature change and intrusion / movement. For example, if the center frequency is set to 1 kHz, 2 kHz, 4 kHz, and 6 kHz, and the frequency interval is set to 4 Hz to obtain a sound field spectrum, the reference sound field spectrum and the danger are large because the frequency shift is large when the center frequency is large in case of temperature change. The correlation coefficient with the sound field spectrum in a situation occurs tends to be proportionally smaller.
However, in the case of intrusion or movement of an object, since such proportional relation is generally inconsistent and irregular, the multiple centers are compared with the one-time measured sound field spectrum compared to the reference sound field spectrum without seeing the temporal change. It is possible to distinguish the intrusion / movement of the object and the change of temperature by observing only the change of the correlation coefficient for each frequency. It may be an embodiment to apply the motion index obtained by expressing the motion index of
In another embodiment of the present invention, as described above, when the analysis section unit of the sound field spectrum is set to 2 to 3 short periods, a rapidly changing sound field spectrum may be selectively detected. Most invasion situations can be detected through this setting by distinguishing from the relatively slow temperature change by simply comparing the change of the correlation coefficient without using the sudden change index of
As various usage examples, the security monitoring apparatus to which the security monitoring method is applied may be connected to an Internet telephone and used as an integrated or external type. The security monitoring method according to an embodiment of the present invention may be applied to various types of smart devices, for example, smart appliances including smart phones, smart TVs, and interphones.
Security monitoring methods using a change pattern of the correlation coefficient of the sound field spectrum according to an embodiment of the present invention does not necessarily require a hardware change of an existing Internet phone or a smart device. In other words, if only the relevant algorithm is embedded in the internal processor, it can be used interlockedly.
Security information detected according to an embodiment of the present invention may be transmitted as multimedia information such as text or video to various smart devices connected to the network. In addition, when a user such as a smartphone or a smart device accesses the relevant security system in the form of an app, it is possible to provide various security related services.
Meanwhile, in the detailed description of the present invention, specific embodiments have been described, but various modifications may be made without departing from the scope of the present invention. Therefore, the scope of the present invention should not be limited to the above-described embodiments, but should be defined by the equivalents of the claims of the present invention as well as the following claims.
110: sound generating device
120: sound receiving device
130: sound field signal processing device
100: security monitoring device
Claims (25)
Outputting a multitone sound wave composed of a linear sum of sinusoids having a plurality of frequency components into a security surveillance space;
Receiving the multitone sound waves and calculating a sound field;
Calculating and storing sound field information for each frequency through the sound field;
Determining whether a sound field change occurs by comparing the reference sound field information for each frequency with the calculated sound field information;
Analyzing whether the sound field change has been collected for a period of time, and distinguishing at least two situations from intrusion, motion, and temperature change based on a correlation between a reference sound field spectrum and a continuous sound field spectrum,
Analyze the temporal variation of the correlation coefficient between the reference sound field spectrum and the continuous sound field spectrum,
If the change pattern is an irregular increase or decrease, it is determined as the movement situation,
And if the change pattern is constantly decreasing gradually, it is determined as the temperature change situation.
And calculating the correlation coefficient by dividing the covariance value of the reference sound field spectrum and the continuous sound field spectrum by the product of standard deviations of each sound field spectrum, and obtaining the correlation by using the derived correlation coefficient.
The determining of whether the change in the sound field occurs comprises comparing the reference sound field spectrum with the current sound field spectrum and determining whether a dangerous situation has occurred.
The predetermined period is a period before the occurrence of the dangerous situation, wherein the temperature change comprises one of a fire, a daily crossover, and a temperature change of air conditioning and heating.
The occurrence of the dangerous situation is determined by comparing the correlation coefficient with a set reference value.
The occurrence of the dangerous situation is determined by comparing the initial correlation coefficient at the time of measurement of the reference sound field spectrum with a limit value from 1 and the correlation coefficient between the reference sound field spectrum and the current sound field spectrum with a limit value from 1.
Further using the change pattern of the correlation coefficient,
If the change pattern is a pattern that rapidly decreases immediately before the occurrence of the dangerous situation, the intrusion situation is determined.
If the change pattern is a pattern that gradually decreases immediately before the occurrence of the dangerous situation, the security monitoring method is determined as the temperature change situation.
The average value of the correlation coefficient between the sound field spectrum and the reference sound field spectrum before the occurrence of the dangerous situation is limited to 1, and the value of the correlation coefficient between the reference sound field spectrum and the reference sound field spectrum at the time of the dangerous situation is limited to 1. And comparing the ratio of the two, the intrusion situation and the temperature change situation is distinguished.
The security monitoring method is determined as the intrusion situation if the change aspect is a rapidly decreasing aspect.
The reference sound field information is the reference sound field spectrum, the calculated sound field information is a current sound field spectrum, the predetermined period is a period before the sound field change occurs, and the movement includes a human movement and an animal movement, and the temperature The method of security monitoring includes the change in the temperature of the fire, daily crossover, and heating and cooling.
The determining of whether the sound field change occurs comprises comparing the correlation coefficient with a set reference value and determining whether the sound field change occurs.
The ratio of the sum of the absolute value of the difference between the correlation coefficients obtained by the continuous measurement in the entire interval and the value obtained by subtracting the correlation coefficient value when the sound field change is detected from 1 is set as a motion index.
And the motion state and the temperature change state are distinguished using the motion index.
Deriving an index representing the degree of frequency shift of the sound field spectrum based on the correlation coefficient,
Security monitoring method for distinguishing the temperature change of fire, daily crossover and heating and cooling in consideration of the direction and duration of the frequency shift.
And detecting the movement of the inside of the security surveillance space to derive sensing information, and transmitting the sensing information to a user's smart device.
If the movement in the security surveillance space is not detected for a predetermined time, the method further comprises: issuing an alarm of an accident occurrence of any one of falling, fainting, and incapacity, and transmitting information on the issued alarm to a server. Security surveillance method.
And selectively detecting only the fire situation in a state where there is the movement inside the security monitoring space, issuing a fire alarm, and transmitting information on the issued fire alarm to a server.
And photographing an image of the security surveillance space and storing the photographed image to verify the security situation when a security situation occurs.
The security surveillance apparatus is linked to a smart home appliances including a security camera having a network function, an internet phone, a smart TV, and an interphone.
Security monitoring method of the user's smart device is linked with the security monitoring device by software without adding any other hardware and the smart device.
When the security situation occurs, the remote control of the smart device to run an app (App) type program and the security monitoring method for notifying the user of the security situation.
A sound wave receiving device receiving the sound wave and calculating a sound field using the sound wave; And
Calculate the sound field spectrum information of the sound field through the continuous measurement, calculate the correlation coefficient between the continuous sound field spectrum information and the reference sound field spectrum information, and analyze the temporal change of the correlation coefficient to analyze the intrusion, motion, A sound field signal processing device for distinguishing at least two of the temperature change situation,
If the change pattern is an irregular increase or decrease, it is determined as the movement situation,
The security monitoring device is determined as the temperature change situation if the change pattern is a pattern that gradually decreases constantly.
And the sound wave is a multitone sound wave consisting of a linear sum of sinusoids having a plurality of frequency components.
And a memory for storing the reference sound field spectrum information.
The sound field signal processing apparatus is configured to calculate a sound pressure or phase of the sound field by using an acoustic transfer function.
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| KR20230016409A (en) * | 2021-07-26 | 2023-02-02 | 최기도 | System for monitoring falldown |
| KR102561631B1 (en) | 2021-07-26 | 2023-07-28 | 최기도 | System for monitoring falldown |
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