CN114360183A - Automatic alarm method and device based on deep learning - Google Patents
Automatic alarm method and device based on deep learning Download PDFInfo
- Publication number
- CN114360183A CN114360183A CN202111635515.5A CN202111635515A CN114360183A CN 114360183 A CN114360183 A CN 114360183A CN 202111635515 A CN202111635515 A CN 202111635515A CN 114360183 A CN114360183 A CN 114360183A
- Authority
- CN
- China
- Prior art keywords
- alarm
- alarm system
- indoor
- current
- face
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The application provides an automatic alarm method and device based on deep learning, and relates to the technical field of data processing. The method comprises the steps of obtaining an indoor image collected by a camera installed indoors; identifying face information in the collected indoor image through a deep learning algorithm, and judging whether the face information is indoor resident personnel or not when the face information exists in the current room; if the face of the indoor resident exists in the recognized face, the alarm is not carried out, and if the face of the indoor resident does not exist in the recognized face, the alarm program is started to carry out alarm processing on the reserved mobile phone number in the alarm system. It can be seen that the embodiment of the application can realize automatic alarm according to the face recognition condition, reduce the loss of property safety and improve the safety of life.
Description
Technical Field
The application relates to the technical field of data processing, in particular to an automatic alarm method and device based on deep learning.
Background
The residential area is a residential area with an independent living environment in a certain area in a city or a town, a set of living service facilities is arranged in the residential area, the residential population of the residential area is concentrated, and the personnel flow amount is large, so that the security problem of the residential area is very important. At present, people are willing to install a camera at home to monitor, so that the loss of articles is avoided, but even if a thief is shot, the thief cannot catch the article easily, and property loss is caused. Therefore, there is a need to solve this technical problem.
Disclosure of Invention
In view of the above problems, the present application is proposed to provide an automatic alarm method and apparatus based on deep learning, which overcomes or at least partially solves the above problems, and can realize automatic alarm, reduce loss of property safety, and improve life safety. The technical scheme is as follows:
in a first aspect, an automatic alarm method based on deep learning is provided, which includes the following steps:
acquiring an indoor image collected by a camera installed indoors;
identifying face information in the collected indoor image through a deep learning algorithm, and judging whether the face information is indoor resident personnel or not when the face information exists in the current room;
if the face of the indoor resident exists in the recognized face, the alarm is not carried out, and if the face of the indoor resident does not exist in the recognized face, the alarm program is started to carry out alarm processing on the reserved mobile phone number in the alarm system.
In one possible implementation, the method further includes:
if the user who receives the reserved mobile phone number selects the non-continuous alarming mode through the operation entrance, the current indoor personnel are legal personnel, and the alarming program is stopped; or
And if the user receiving the reserved mobile phone number selects a continuous alarm mode through the operation entrance, the current indoor personnel are illegal personnel, the intelligent door lock is controlled to be normally closed through the intelligent home system to ensure that the illegal personnel can not exit through the door lock, and then the alarm system dials an alarm telephone to inform that the illegal family entry behavior exists currently and carries the current address.
In a possible implementation manner, step a1 is included, and the switch of the alarm system is controlled according to the recognition result of recognizing the face information in the collected indoor image through the deep learning algorithm by using the following formula:
wherein h (t) represents a switch control value of the alarm system at the present moment; t represents the current time; f (i) information data in a matrix form representing the ith human face in the indoor image acquired by using an image recognition algorithm at the current moment; f (a) information data in a matrix form representing the human face of the a-th indoor resident; m represents the total number of indoor resident persons; p [ f (i), f (a) ] represent output values obtained by identifying and comparing information data of two faces in parentheses by using a deep learning algorithm, and if the two face identification matching results in parentheses are the same person, P [ f (i), f (a)) ] is 1, otherwise, P [ f (i), f (a)) ] is 0; n (t) represents the total number of faces in the image acquired by the image recognition algorithm at the current moment, and if no face is acquired, n (t) is 0; Λ represents a logical relationship and; v represents a logical relationship or;
if h (t) is 0, the switch of the alarm system is in a closed state at the current moment and is in a normally closed state until an opening signal is received;
and if h (t) is 1, the switch of the alarm system is in an open state at the current moment and is in a normally open state until a closing signal is received.
In a possible implementation manner, step a2 is included, if the switch of the alarm system is already turned on, the following formula is used to determine whether the user of the reserved mobile phone number selects at the operation entry, and the frequency of sending the short message to the reserved mobile phone number by the alarm system is controlled at the current time, so as to ensure that the user of the reserved mobile phone number can send the alarm short message with high frequency repeatedly when the operation entry is not selected:
wherein f (t) represents the frequency of sending short messages to the reserved mobile phone number by the alarm system at the current moment; t is t0Indicating the moment when the alarm system is turned on; g (t)0T) represents the output from t0The user reserving the mobile phone number from the moment to the current moment selects the value at the operation entrance, and if the alarm mode is not continued, G (t) is output0When t is 1, G (t) is output if the continuous alarm mode is selected0T is-1, and if no selection is made, G (t) is output0～t)＝0；D[]The numerical value is represented as a zero test function, when the numerical value in the brackets is 0, the functional value is 1, otherwise, the functional value is 0; t is0And the time required by the alarm system to send an alarm short message to the reserved mobile phone number is represented.
In a possible implementation manner, the method includes step a3, and the following formulas are used to control whether the alarm system needs to be closed and control the intelligent door lock in the intelligent home system according to the selection condition of the user who reserves the mobile phone number at the operation entrance:
E(t)＝D[1-G(t0～t)]-D[1+G(t0～t)]
wherein E (t) represents a closing control value of the alarm system and a control value of an intelligent door lock in the intelligent home system;
if E (t) is 1, the alarm system needs to be controlled to be closed, the intelligent door lock in the intelligent home system is not controlled, the current intelligent door lock state is maintained, the information data of the current human face is not watched, and the judgment and control of the steps A1 to A3 are carried out again after the indoor unmanned state is identified;
if E (t) is 0, the alarm system is not controlled, the current state of the alarm system is maintained, the intelligent door lock in the intelligent home system is not controlled, and the current state of the intelligent door lock is maintained;
and if E (t) is equal to-1, controlling the alarm system to be continuously opened, dialing an alarm telephone to inform that an illegal family entry behavior exists currently and carrying a current address, and controlling an intelligent door lock in the intelligent home system to be normally closed so as to ensure that an illegal person cannot exit through the door lock.
In a second aspect, an automatic alarm device based on deep learning is provided, which includes:
the acquisition module is used for acquiring indoor images collected by a camera installed indoors;
the recognition module is used for recognizing the face information in the collected indoor image through a deep learning algorithm, and judging whether the face information is indoor resident personnel or not when the face information exists in the current indoor environment;
and the alarm module is used for not giving an alarm if the face of the indoor resident exists in the recognized face, and starting an alarm program if the face of the indoor resident does not exist in the recognized face to give an alarm to the reserved mobile phone number in the alarm system.
In one possible implementation, the alarm module is further configured to:
if the user who receives the reserved mobile phone number selects the non-continuous alarming mode through the operation entrance, the current indoor personnel are legal personnel, and the alarming program is stopped; or
And if the user receiving the reserved mobile phone number selects a continuous alarm mode through the operation entrance, the current indoor personnel are illegal personnel, the intelligent door lock is controlled to be normally closed through the intelligent home system to ensure that the illegal personnel can not exit through the door lock, and then the alarm system dials an alarm telephone to inform that the illegal family entry behavior exists currently and carries the current address.
In one possible implementation, the alarm module is further configured to:
step A1, controlling the switch of the alarm system according to the recognition result of recognizing the face information in the collected indoor image by the deep learning algorithm by using the following formula:
wherein h (t) represents a switch control value of the alarm system at the present moment; t represents the current time; f (i) information data in a matrix form representing the ith human face in the indoor image acquired by using an image recognition algorithm at the current moment; f (a) information data in a matrix form representing the human face of the a-th indoor resident; m represents the total number of indoor resident persons; p [ f (i), f (a) ] represent output values obtained by identifying and comparing information data of two faces in parentheses by using a deep learning algorithm, and if the two face identification matching results in parentheses are the same person, P [ f (i), f (a)) ] is 1, otherwise, P [ f (i), f (a)) ] is 0; n (t) represents the total number of faces in the image acquired by the image recognition algorithm at the current moment, and if no face is acquired, n (t) is 0; Λ represents a logical relationship and; v represents a logical relationship or;
if h (t) is 0, the switch of the alarm system is in a closed state at the current moment and is in a normally closed state until an opening signal is received;
and if h (t) is 1, the switch of the alarm system is in an open state at the current moment and is in a normally open state until a closing signal is received.
In one possible implementation, the alarm module is further configured to:
step A2, if the switch of the alarm system is opened, the following formula is used to control the frequency of the alarm system to send short messages to the reserved mobile phone number according to whether the user of the reserved mobile phone number selects at the operation entrance or not and the current time, so as to ensure that the user of the reserved mobile phone number can send alarm short messages with high frequency repeatedly when the operation entrance is not selected:
wherein f (t) represents the frequency of sending short messages to the reserved mobile phone number by the alarm system at the current moment; t is t0Indicating the moment when the alarm system is turned on; g (t)0T) represents the output from t0The user reserving the mobile phone number from the moment to the current moment selects the value at the operation entrance, and if the alarm mode is not continued, G (t) is output0When t is 1, G (t) is output if the continuous alarm mode is selected0T is-1, and if no selection is made, G (t) is output0～t)＝0；D[]The numerical value is represented as a zero test function, when the numerical value in the brackets is 0, the functional value is 1, otherwise, the functional value is 0; t is0And the time required by the alarm system to send an alarm short message to the reserved mobile phone number is represented.
In one possible implementation, the alarm module is further configured to:
step A3, controlling whether the alarm system needs to be closed and controlling the intelligent door lock in the intelligent home system according to the selection condition of the user reserving the mobile phone number at the operation entrance by using the following formula:
E(t)＝D[1-G(t0～t)]-D[1+G(t0～t)]
wherein E (t) represents a closing control value of the alarm system and a control value of an intelligent door lock in the intelligent home system;
if E (t) is 1, the alarm system needs to be controlled to be closed, the intelligent door lock in the intelligent home system is not controlled, the current intelligent door lock state is maintained, the information data of the current human face is not watched, and the judgment and control of the steps A1 to A3 are carried out again after the indoor unmanned state is identified;
if E (t) is 0, the alarm system is not controlled, the current state of the alarm system is maintained, the intelligent door lock in the intelligent home system is not controlled, and the current state of the intelligent door lock is maintained;
and if E (t) is equal to-1, controlling the alarm system to be continuously opened, dialing an alarm telephone to inform that an illegal family entry behavior exists currently and carrying a current address, and controlling an intelligent door lock in the intelligent home system to be normally closed so as to ensure that an illegal person cannot exit through the door lock.
By means of the technical scheme, the automatic alarm method and device based on deep learning provided by the embodiment of the application firstly obtain indoor images collected by a camera installed indoors; identifying face information in the collected indoor image through a deep learning algorithm, and judging whether the face information is indoor resident personnel or not when the face information exists in the current room; if the face of the indoor resident exists in the recognized face, the alarm is not carried out, and if the face of the indoor resident does not exist in the recognized face, the alarm program is started to carry out alarm processing on the reserved mobile phone number in the alarm system. It can be seen that the embodiment of the application can realize automatic alarm according to the face recognition condition, reduce the loss of property safety and improve the safety of life.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments of the present application will be briefly described below.
FIG. 1 shows a flow diagram of a deep learning based automatic alert method according to an embodiment of the present application;
fig. 2 is a block diagram illustrating an automatic warning apparatus based on deep learning according to an embodiment of the present application.
Detailed Description
Exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that such uses are interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the term "include" and its variants are to be read as open-ended terms meaning "including, but not limited to".
The embodiment of the application provides an automatic alarm method based on deep learning, which can be applied to electronic equipment such as a mobile terminal, a personal computer and a tablet computer. As shown in fig. 1, the automatic alarm method based on deep learning may include the following steps S101 to S103:
step S101, acquiring an indoor image collected by a camera installed indoors;
step S102, identifying face information in the collected indoor image through a deep learning algorithm, and judging whether the face information is indoor resident personnel or not when the face information exists in the current room;
and step S103, if the face of the indoor resident exists in the recognized face, no alarm is given, and if the face of the indoor resident does not exist in the recognized face, an alarm program is started to give an alarm to the reserved mobile phone number in the alarm system.
The deep learning algorithm mentioned in the embodiment of the application can be the existing human face recognition artificial intelligence algorithm, and the indoor image is input into the human face recognition artificial intelligence algorithm to recognize the human face in the indoor image.
The method includes the steps that firstly, indoor images collected by a camera installed indoors are obtained; identifying face information in the collected indoor image through a deep learning algorithm, and judging whether the face information is indoor resident personnel or not when the face information exists in the current room; if the face of the indoor resident exists in the recognized face, the alarm is not carried out, and if the face of the indoor resident does not exist in the recognized face, the alarm program is started to carry out alarm processing on the reserved mobile phone number in the alarm system. It can be seen that the embodiment of the application can realize automatic alarm according to the face recognition condition, reduce the loss of property safety and improve the safety of life.
The embodiment of the present application provides a possible implementation manner, and the step S103 of performing alarm processing on the mobile phone number reserved in the alarm system may specifically be performing short message alarm on the mobile phone number reserved in the alarm system. After short message alarm is carried out on the mobile phone number reserved in the alarm system, the method also comprises the following steps of a1 or a 2:
step a1, receiving the non-continuous alarming mode selected by the user with the reserved mobile phone number through the operation entrance, and stopping the alarming program if the current indoor personnel are legal personnel;
step a2, receiving a continuous alarm mode selected by a user with a reserved mobile phone number through an operation entrance, controlling the intelligent door lock to normally close when the current indoor personnel are illegal personnel through the intelligent home system so as to ensure that the illegal personnel can not exit through the door lock, and then dialing an alarm telephone by the alarm system to inform that the current illegal entry behavior exists and the current address is carried.
In the embodiment of the present application, the operation entry may be a virtual key of a display interface of an APP (application). The user who reserves the cell-phone number can control alarm system through APP after discovering the SMS, and the control mode includes two kinds, and one kind is not to continue the alarm mode, and another kind is to continue the alarm mode. If the user who reserves the mobile phone number chooses not to continue the alarm mode in the APP, the system can regard the current indoor personnel as legal personnel, the alarm program is stopped, if the user who reserves the mobile phone number chooses to continue the alarm mode in the APP, the system can regard the current indoor personnel as illegal personnel, the intelligent door lock is controlled to be normally closed through the intelligent home system, so that the illegal personnel can not exit through the door lock, then the alarm system dials an alarm phone to inform that the current behavior of illegal entry exists and attaches the current address.
The embodiment of the present application provides a possible implementation manner, which may further include a step a1, where the following formula is used to control a switch of an alarm system according to an identification result obtained by identifying face information in an acquired indoor image through a deep learning algorithm:
wherein h (t) represents the switch control value of the alarm system at the current moment; t represents the current time; f (i) information data in a matrix form representing the ith human face in the indoor image acquired by using an image recognition algorithm at the current moment; f (a) information data in a matrix form representing the human face of the a-th indoor resident; m represents the total number of indoor resident persons; p [ f (i), f (a) ] represent output values obtained by identifying and comparing information data of two faces in parentheses by using a deep learning algorithm, and if the two face identification matching results in parentheses are the same person, P [ f (i), f (a)) ] is 1, otherwise, P [ f (i), f (a)) ] is 0; n (t) represents the total number of faces in the image acquired by the image recognition algorithm at the current moment, and if no face is acquired, n (t) is 0; Λ represents a logical relationship and; v represents a logical relationship or;
if h (t) is 0, the switch of the alarm system at the current moment is in a closed state and is in a normally closed state until an opening signal is received;
and if h (t) is 1, the switch of the alarm system is in an open state at the current moment and is in a normally open state until a closing signal is received.
According to the embodiment of the application, the formula in the step A1 is utilized to control the on-off of the alarm system according to the recognition result of recognizing the face information in the collected indoor image through the deep learning algorithm, so that the alarm system is not triggered when no person exists in the room or resident persons exist in the room, and the reliability of the system is ensured.
The embodiment of the present application provides a possible implementation manner, which may further include step a2, where if the switch of the alarm system is turned on, the following formula is used to determine whether the user who reserves the mobile phone number selects at the operation entry, and the frequency of sending the short message to the reserved mobile phone number by the alarm system is controlled at the current moment, so as to ensure that the user who reserves the mobile phone number can send the alarm short message with high frequency repeatedly when the operation entry is not selected:
wherein, f (t) represents the frequency of sending short messages to the reserved mobile phone number by the alarm system at the current moment; t is t0Presentation alarm systemThe moment of opening; g (t)0T) represents the output from t0The user reserving the mobile phone number from the moment to the current moment selects the value at the operation entrance, and if the alarm mode is not continued, G (t) is output0When t is 1, G (t) is output if the continuous alarm mode is selected0T is-1, and if no selection is made, G (t) is output0～t)＝0；D[]The numerical value is represented as a zero test function, when the numerical value in the brackets is 0, the functional value is 1, otherwise, the functional value is 0; t is0And the time required by the alarm system to send an alarm short message to the reserved mobile phone number is represented.
The operation entry can be a virtual key of a display interface of the APP, whether the user who reserves the mobile phone number selects in the APP or not can be determined by the formula of the step A2, and the frequency of the short message sent by the alarm system to the reserved mobile phone number is controlled at the current moment, so that the high-frequency sending of the alarm short message can be repeated when the user who reserves the mobile phone number does not select in the APP.
The embodiment of the application provides a possible implementation manner, which may further include a step a3, where whether the alarm system needs to be closed and the control of the intelligent door lock in the intelligent home system are controlled according to the selection condition of the user who reserves the mobile phone number at the operation entrance by using the following formula:
E(t)＝D[1-G(t0～t)]-D[1+G(t0～t)]
wherein E (t) represents a closing control value of the alarm system and a control value of an intelligent door lock in the intelligent home system;
if E (t) is 1, the alarm system needs to be controlled to be closed, the intelligent door lock in the intelligent home system is not controlled, the current intelligent door lock state is maintained, the information data of the current human face is not looked at, and the judgment and the control of the steps A1 to A3 are carried out again after the indoor unmanned state is identified;
if E (t) is 0, the alarm system is not controlled, the current state of the alarm system is maintained, the intelligent door lock in the intelligent home system is not controlled, and the current state of the intelligent door lock is maintained;
and if E (t) is equal to-1, controlling the alarm system to be continuously opened, dialing an alarm telephone to inform that an illegal family entry behavior exists currently and carrying a current address, and controlling an intelligent door lock in the intelligent home system to be normally closed so as to ensure that an illegal person cannot exit through the door lock.
According to the method and the device, whether the alarm system needs to be closed and the intelligent door lock in the intelligent home system are controlled according to the selection condition of the user with the reserved mobile phone number in the APP by the formula of the step A3, firstly, the alarm is timely given and the position is informed when the illegal user is confirmed to break in, secondly, the intelligent door lock is normally closed by the intelligent home, so that the illegal person can not pass through the door lock to be ensured, and the method and the device are favorable for assisting police to arrest criminals.
It should be noted that, in practical applications, all the possible embodiments described above may be combined in a combined manner at will to form possible embodiments of the present application, and details are not described here again.
Based on the automatic alarm method based on deep learning provided by each embodiment, the embodiment of the application also provides an automatic alarm device based on deep learning based on the same inventive concept.
Fig. 2 is a block diagram illustrating an automatic warning apparatus based on deep learning according to an embodiment of the present application. As shown in fig. 2, the automatic alarm device based on deep learning may include an acquisition module 210, an identification module 220, and an alarm module 230.
An obtaining module 210, configured to obtain an indoor image collected by an indoor camera;
the recognition module 220 is configured to recognize face information in the collected indoor image through a deep learning algorithm, and when it is recognized that the face information exists indoors, determine whether the face information is an indoor resident;
and the alarm module 230 is configured to not alarm if the face of the indoor resident exists in the identified face, and start an alarm program to alarm the mobile phone number reserved in the alarm system if the face of the indoor resident does not exist in the identified face.
In the embodiment of the present application, a possible implementation manner is provided, and the alarm module 230 shown in fig. 2 is further configured to:
if the user who receives the reserved mobile phone number selects the non-continuous alarming mode through the operation entrance, the current indoor personnel are legal personnel, and the alarming program is stopped; or
And if the user receiving the reserved mobile phone number selects a continuous alarm mode through the operation entrance, the current indoor personnel are illegal personnel, the intelligent door lock is controlled to be normally closed through the intelligent home system to ensure that the illegal personnel can not exit through the door lock, and then the alarm system dials an alarm telephone to inform that the illegal family entry behavior exists currently and carries the current address.
In the embodiment of the present application, a possible implementation manner is provided, and the alarm module 230 shown in fig. 2 is further configured to:
step A1, controlling the switch of the alarm system according to the recognition result of the face information in the collected indoor image through the deep learning algorithm by using the following formula:
wherein h (t) represents the switch control value of the alarm system at the current moment; t represents the current time; f (i) information data in a matrix form representing the ith human face in the indoor image acquired by using an image recognition algorithm at the current moment; f (a) information data in a matrix form representing the human face of the a-th indoor resident; m represents the total number of indoor resident persons; p [ f (i), f (a) ] represent output values obtained by identifying and comparing information data of two faces in parentheses by using a deep learning algorithm, and if the two face identification matching results in parentheses are the same person, P [ f (i), f (a)) ] is 1, otherwise, P [ f (i), f (a)) ] is 0; n (t) represents the total number of faces in the image acquired by the image recognition algorithm at the current moment, and if no face is acquired, n (t) is 0; Λ represents a logical relationship and; v represents a logical relationship or;
if h (t) is 0, the switch of the alarm system at the current moment is in a closed state and is in a normally closed state until an opening signal is received;
and if h (t) is 1, the switch of the alarm system is in an open state at the current moment and is in a normally open state until a closing signal is received.
In the embodiment of the present application, a possible implementation manner is provided, and the alarm module 230 shown in fig. 2 is further configured to:
step A2, if the switch of the alarm system is opened, the following formula is used to control the frequency of the alarm system to send short messages to the reserved mobile phone number according to whether the user of the reserved mobile phone number selects at the operation entrance or not and the current time, so as to ensure that the user of the reserved mobile phone number can send alarm short messages with high frequency repeatedly when the operation entrance is not selected:
wherein, f (t) represents the frequency of sending short messages to the reserved mobile phone number by the alarm system at the current moment; t is t0Indicating the starting time of the alarm system; g (t)0T) represents the output from t0The user reserving the mobile phone number from the moment to the current moment selects the value at the operation entrance, and if the alarm mode is not continued, G (t) is output0When t is 1, G (t) is output if the continuous alarm mode is selected0T is-1, and if no selection is made, G (t) is output0～t)＝0；D[]The numerical value is represented as a zero test function, when the numerical value in the brackets is 0, the functional value is 1, otherwise, the functional value is 0; t is0And the time required by the alarm system to send an alarm short message to the reserved mobile phone number is represented.
In the embodiment of the present application, a possible implementation manner is provided, and the alarm module 230 shown in fig. 2 is further configured to:
step A3, controlling whether the alarm system needs to be closed and controlling the intelligent door lock in the intelligent home system according to the selection condition of the user reserving the mobile phone number at the operation entrance by using the following formula:
E(t)＝D[1-G(t0～t)]-D[1+G(t0～t)]
wherein E (t) represents a closing control value of the alarm system and a control value of an intelligent door lock in the intelligent home system;
if E (t) is 1, the alarm system needs to be controlled to be closed, the intelligent door lock in the intelligent home system is not controlled, the current intelligent door lock state is maintained, the information data of the current human face is not looked at, and the judgment and the control of the steps A1 to A3 are carried out again after the indoor unmanned state is identified;
if E (t) is 0, the alarm system is not controlled, the current state of the alarm system is maintained, the intelligent door lock in the intelligent home system is not controlled, and the current state of the intelligent door lock is maintained;
and if E (t) is equal to-1, controlling the alarm system to be continuously opened, dialing an alarm telephone to inform that an illegal family entry behavior exists currently and carrying a current address, and controlling an intelligent door lock in the intelligent home system to be normally closed so as to ensure that an illegal person cannot exit through the door lock.
The automatic alarm device based on deep learning provided by the embodiment of the application comprises the following steps of firstly, acquiring an indoor image collected by a camera installed indoors; identifying face information in the collected indoor image through a deep learning algorithm, and judging whether the face information is indoor resident personnel or not when the face information exists in the current room; if the face of the indoor resident exists in the recognized face, the alarm is not carried out, and if the face of the indoor resident does not exist in the recognized face, the alarm program is started to carry out alarm processing on the reserved mobile phone number in the alarm system. It can be seen that the embodiment of the application can realize automatic alarm according to the face recognition condition, reduce the loss of property safety and improve the safety of life.
It can be clearly understood by those skilled in the art that the specific working processes of the system, the apparatus, and the module described above may refer to the corresponding processes in the foregoing method embodiments, and for the sake of brevity, the detailed description is omitted here.
Those of ordinary skill in the art will understand that: the technical solution of the present application may be essentially or wholly or partially embodied in the form of a software product, where the computer software product is stored in a storage medium and includes program instructions for enabling an electronic device (e.g., a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application when the program instructions are executed. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Alternatively, all or part of the steps of implementing the foregoing method embodiments may be implemented by hardware (an electronic device such as a personal computer, a server, or a network device) associated with program instructions, which may be stored in a computer-readable storage medium, and when the program instructions are executed by a processor of the electronic device, the electronic device executes all or part of the steps of the method described in the embodiments of the present application.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments can be modified or some or all of the technical features can be equivalently replaced within the spirit and principle of the present application; such modifications or substitutions do not depart from the scope of the present application.
Claims (10)
1. An automatic alarm method based on deep learning is characterized by comprising the following steps:
acquiring an indoor image collected by a camera installed indoors;
identifying face information in the collected indoor image through a deep learning algorithm, and judging whether the face information is indoor resident personnel or not when the face information exists in the current room;
if the face of the indoor resident exists in the recognized face, the alarm is not carried out, and if the face of the indoor resident does not exist in the recognized face, the alarm program is started to carry out alarm processing on the reserved mobile phone number in the alarm system.
2. The deep learning based automatic alarm method according to claim 1, characterized in that the method further comprises:
if the user who receives the reserved mobile phone number selects the non-continuous alarming mode through the operation entrance, the current indoor personnel are legal personnel, and the alarming program is stopped; or
And if the user receiving the reserved mobile phone number selects a continuous alarm mode through the operation entrance, the current indoor personnel are illegal personnel, the intelligent door lock is controlled to be normally closed through the intelligent home system to ensure that the illegal personnel can not exit through the door lock, and then the alarm system dials an alarm telephone to inform that the illegal family entry behavior exists currently and carries the current address.
3. The automatic alarm method based on deep learning of claim 2, wherein the method comprises a step a1, in which the alarm system is controlled to be switched on and off according to the recognition result of the human face information in the collected indoor image through the deep learning algorithm by using the following formula:
wherein h (t) represents a switch control value of the alarm system at the present moment; t represents the current time; f (i) information data in a matrix form representing the ith human face in the indoor image acquired by using an image recognition algorithm at the current moment; f (a) information data in a matrix form representing the human face of the a-th indoor resident; m represents the total number of indoor resident persons; p [ f (i), f (a) ] represent output values obtained by identifying and comparing information data of two faces in parentheses by using a deep learning algorithm, and if the two face identification matching results in parentheses are the same person, P [ f (i), f (a)) ] is 1, otherwise, P [ f (i), f (a)) ] is 0; n (t) represents the total number of faces in the image acquired by the image recognition algorithm at the current moment, and if no face is acquired, n (t) is 0; Λ represents a logical relationship and; the V-shaped represents a logical relationship or;
if h (t) is 0, the switch of the alarm system is in a closed state at the current moment and is in a normally closed state until an opening signal is received;
and if h (t) is 1, the switch of the alarm system is in an open state at the current moment and is in a normally open state until a closing signal is received.
4. The automatic alarm method based on deep learning of claim 3, comprising a step A2, wherein if the switch of the alarm system is turned on, the following formula is used to control the frequency of sending short messages to the reserved mobile phone number by the alarm system according to whether the user of the reserved mobile phone number selects at the operation entrance or not, so as to ensure that the user of the reserved mobile phone number can repeatedly send alarm short messages with high frequency when the operation entrance is not selected:
wherein f (t) represents the frequency of sending short messages to the reserved mobile phone number by the alarm system at the current moment; t is t0Indicating the moment when the alarm system is turned on; g (t)0T) represents the output from t0The user reserving the mobile phone number from the moment to the current moment selects the value at the operation entrance, and if the alarm mode is not continued, G (t) is output0When t is 1, G (t) is output if the continuous alarm mode is selected0T is-1, and if no selection is made, G (t) is output0～t)＝0；D[]The numerical value is represented as a zero test function, when the numerical value in the brackets is 0, the functional value is 1, otherwise, the functional value is 0; t is0And the time required by the alarm system to send an alarm short message to the reserved mobile phone number is represented.
5. The automatic alarm method based on deep learning of claim 4, wherein the method comprises a step A3, and the following formulas are used for controlling whether the alarm system needs to be closed and controlling the intelligent door lock in the intelligent home system according to the selection condition of the user with the reserved mobile phone number at the operation entrance:
E(t)＝D[1-G(t0～t)]-D[1+G(t0～t)]
wherein E (t) represents a closing control value of the alarm system and a control value of an intelligent door lock in the intelligent home system;
if E (t) is 1, the alarm system needs to be controlled to be closed, the intelligent door lock in the intelligent home system is not controlled, the current intelligent door lock state is maintained, the information data of the current human face is not watched, and the judgment and control of the steps A1 to A3 are carried out again after the indoor unmanned state is identified;
if E (t) is 0, the alarm system is not controlled, the current state of the alarm system is maintained, the intelligent door lock in the intelligent home system is not controlled, and the current state of the intelligent door lock is maintained;
and if E (t) is equal to-1, controlling the alarm system to be continuously opened, dialing an alarm telephone to inform that an illegal family entry behavior exists currently and carrying a current address, and controlling an intelligent door lock in the intelligent home system to be normally closed so as to ensure that an illegal person cannot exit through the door lock.
6. An automatic alarm device based on deep learning, comprising:
the acquisition module is used for acquiring indoor images collected by a camera installed indoors;
the recognition module is used for recognizing the face information in the collected indoor image through a deep learning algorithm, and judging whether the face information is indoor resident personnel or not when the face information exists in the current indoor environment;
and the alarm module is used for not giving an alarm if the face of the indoor resident exists in the recognized face, and starting an alarm program if the face of the indoor resident does not exist in the recognized face to give an alarm to the reserved mobile phone number in the alarm system.
7. The deep learning based automatic warning device according to claim 6, wherein the warning module is further configured to:
if the user who receives the reserved mobile phone number selects the non-continuous alarming mode through the operation entrance, the current indoor personnel are legal personnel, and the alarming program is stopped; or
And if the user receiving the reserved mobile phone number selects a continuous alarm mode through the operation entrance, the current indoor personnel are illegal personnel, the intelligent door lock is controlled to be normally closed through the intelligent home system to ensure that the illegal personnel can not exit through the door lock, and then the alarm system dials an alarm telephone to inform that the illegal family entry behavior exists currently and carries the current address.
8. The deep learning based automatic alert device of claim 7, wherein the alert module is further configured to:
step A1, controlling the switch of the alarm system according to the recognition result of recognizing the face information in the collected indoor image by the deep learning algorithm by using the following formula:
wherein h (t) represents a switch control value of the alarm system at the present moment; t represents the current time; f (i) information data in a matrix form representing the ith human face in the indoor image acquired by using an image recognition algorithm at the current moment; f (a) information data in a matrix form representing the human face of the a-th indoor resident; m represents the total number of indoor resident persons; p [ f (i), f (a) ] represent output values obtained by identifying and comparing information data of two faces in parentheses by using a deep learning algorithm, and if the two face identification matching results in parentheses are the same person, P [ f (i), f (a)) ] is 1, otherwise, P [ f (i), f (a)) ] is 0; n (t) represents the total number of faces in the image acquired by the image recognition algorithm at the current moment, and if no face is acquired, n (t) is 0; Λ represents a logical relationship and; the V-shaped represents a logical relationship or;
if h (t) is 0, the switch of the alarm system is in a closed state at the current moment and is in a normally closed state until an opening signal is received;
and if h (t) is 1, the switch of the alarm system is in an open state at the current moment and is in a normally open state until a closing signal is received.
9. The deep learning based automatic alert device of claim 8, wherein the alert module is further configured to:
step A2, if the switch of the alarm system is opened, the following formula is used to control the frequency of the alarm system to send short messages to the reserved mobile phone number according to whether the user of the reserved mobile phone number selects at the operation entrance or not and the current time, so as to ensure that the user of the reserved mobile phone number can send alarm short messages with high frequency repeatedly when the operation entrance is not selected:
wherein f (t) represents the frequency of sending short messages to the reserved mobile phone number by the alarm system at the current moment; t is t0Indicating the moment when the alarm system is turned on; g (t)0T) represents the output from t0The user reserving the mobile phone number from the moment to the current moment selects the value at the operation entrance, and if the alarm mode is not continued, G (t) is output0When t is 1, G (t) is output if the continuous alarm mode is selected0T is-1, and if no selection is made, G (t) is output0～t)＝0；D[]The numerical value is represented as a zero test function, when the numerical value in the brackets is 0, the functional value is 1, otherwise, the functional value is 0; t is0And the time required by the alarm system to send an alarm short message to the reserved mobile phone number is represented.
10. The deep learning based automatic alert device of claim 9, wherein the alert module is further configured to:
step A3, controlling whether the alarm system needs to be closed and controlling the intelligent door lock in the intelligent home system according to the selection condition of the user reserving the mobile phone number at the operation entrance by using the following formula:
E(t)＝D[1-G(t0～t)]-D[1+G(t0～t)]
wherein E (t) represents a closing control value of the alarm system and a control value of an intelligent door lock in the intelligent home system;
if E (t) is 1, the alarm system needs to be controlled to be closed, the intelligent door lock in the intelligent home system is not controlled, the current intelligent door lock state is maintained, the information data of the current human face is not watched, and the judgment and control of the steps A1 to A3 are carried out again after the indoor unmanned state is identified;
if E (t) is 0, the alarm system is not controlled, the current state of the alarm system is maintained, the intelligent door lock in the intelligent home system is not controlled, and the current state of the intelligent door lock is maintained;
and if E (t) is equal to-1, controlling the alarm system to be continuously opened, dialing an alarm telephone to inform that an illegal family entry behavior exists currently and carrying a current address, and controlling an intelligent door lock in the intelligent home system to be normally closed so as to ensure that an illegal person cannot exit through the door lock.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111635515.5A CN114360183B (en) | 2021-12-29 | 2021-12-29 | Automatic alarm method and device based on deep learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111635515.5A CN114360183B (en) | 2021-12-29 | 2021-12-29 | Automatic alarm method and device based on deep learning |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114360183A true CN114360183A (en) | 2022-04-15 |
| CN114360183B CN114360183B (en) | 2022-08-19 |
Family
ID=81102988
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111635515.5A Active CN114360183B (en) | 2021-12-29 | 2021-12-29 | Automatic alarm method and device based on deep learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114360183B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010282315A (en) * | 2009-06-03 | 2010-12-16 | Toyota Motor Corp | Crime prevention system |
| CN108364374A (en) * | 2017-12-28 | 2018-08-03 | 武汉烽火众智数字技术有限责任公司 | Face access control device based on deep learning and method |
| CN108986281A (en) * | 2018-07-20 | 2018-12-11 | 清华大学苏州汽车研究院（吴江） | Kinsfolk's intelligent identification system based on recognition of face |
| CN210039015U (en) * | 2019-07-10 | 2020-02-07 | 中铁四局集团建筑工程有限公司 | Dangerous source grading alarm device based on video analysis and face recognition |
| CN111768584A (en) * | 2020-06-29 | 2020-10-13 | 侯易超 | Intelligent safety system capable of automatically alarming in real time when people enter home |
| CN213070103U (en) * | 2020-09-09 | 2021-04-27 | 北京达佳互联信息技术有限公司 | Safety monitoring system |
| CN112767569A (en) * | 2020-12-18 | 2021-05-07 | 山东盛帆蓝海电气有限公司 | Intelligent building personnel monitoring system and method based on face recognition |
-
2021
- 2021-12-29 CN CN202111635515.5A patent/CN114360183B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010282315A (en) * | 2009-06-03 | 2010-12-16 | Toyota Motor Corp | Crime prevention system |
| CN108364374A (en) * | 2017-12-28 | 2018-08-03 | 武汉烽火众智数字技术有限责任公司 | Face access control device based on deep learning and method |
| CN108986281A (en) * | 2018-07-20 | 2018-12-11 | 清华大学苏州汽车研究院（吴江） | Kinsfolk's intelligent identification system based on recognition of face |
| CN210039015U (en) * | 2019-07-10 | 2020-02-07 | 中铁四局集团建筑工程有限公司 | Dangerous source grading alarm device based on video analysis and face recognition |
| CN111768584A (en) * | 2020-06-29 | 2020-10-13 | 侯易超 | Intelligent safety system capable of automatically alarming in real time when people enter home |
| CN213070103U (en) * | 2020-09-09 | 2021-04-27 | 北京达佳互联信息技术有限公司 | Safety monitoring system |
| CN112767569A (en) * | 2020-12-18 | 2021-05-07 | 山东盛帆蓝海电气有限公司 | Intelligent building personnel monitoring system and method based on face recognition |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114360183B (en) | 2022-08-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10902707B1 (en) | Video monitoring and alarm verification technology | |
| US9666047B2 (en) | Fixed property monitoring with moving asset location tracking | |
| US10049560B1 (en) | Handling duress input | |
| US10986717B1 (en) | Reduced false alarms for outdoor IP security cameras | |
| CN105632001B (en) | A kind of security-protecting and monitoring method and system based on recognition of face | |
| US8675071B1 (en) | Video monitoring and alarm verification technology | |
| WO2018232922A1 (en) | Home security method and system | |
| JP2003508655A (en) | Fingerprint recognition door lock system control method | |
| US10061273B2 (en) | Intelligent security hub for providing smart alerts | |
| CN108600202A (en) | A kind of information processing method and device, computer readable storage medium | |
| CN104580685A (en) | Terminal state processing method and device | |
| CN111243142A (en) | Failure processing method and device for intelligent door lock and storage medium | |
| CN107105099A (en) | A kind of mobile terminal alarm method and its device, electronic equipment | |
| CN109410386A (en) | Unlocking processing method and processing device, storage medium, processor | |
| CN108229157A (en) | Server attack early warning method and apparatus | |
| CN107396368B (en) | Mobile terminal, fraud prevention prompt processing method thereof and storage medium | |
| CN114360183B (en) | Automatic alarm method and device based on deep learning | |
| CN114268767A (en) | Emergency call method, emergency call device, storage medium and electronic equipment | |
| CN110288731A (en) | A kind of unlocking method, device and electronic lock | |
| CN212229720U (en) | Intelligent access control system based on block chain | |
| CN111583493A (en) | Intelligent access control system based on block chain | |
| JP5364654B2 (en) | Apartment house intercom system | |
| CN102982267A (en) | Safety protection method and system and terminal | |
| CN110099255A (en) | Security system control method and device | |
| CN105100120B (en) | Monitoring method, device and the terminal of client identification module |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |