CN112911204A - Monitoring method, monitoring device, storage medium and electronic equipment - Google Patents
Monitoring method, monitoring device, storage medium and electronic equipment Download PDFInfo
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- CN112911204A CN112911204A CN201911217251.4A CN201911217251A CN112911204A CN 112911204 A CN112911204 A CN 112911204A CN 201911217251 A CN201911217251 A CN 201911217251A CN 112911204 A CN112911204 A CN 112911204A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/183—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a single remote source
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/103—Static body considered as a whole, e.g. static pedestrian or occupant recognition
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
- G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
- G08B13/19602—Image analysis to detect motion of the intruder, e.g. by frame subtraction
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/695—Control of camera direction for changing a field of view, e.g. pan, tilt or based on tracking of objects
Abstract
The monitoring method, the monitoring device, the storage medium and the electronic equipment collect monitoring images; when detecting that the monitoring image comprises a human body, identifying whether the identity of the human body is a stranger; if yes, recording the monitoring video of the stranger according to preset time length, and sending the monitoring video and the alarm prompt information to the user terminal. According to the technical scheme, under the condition that a stranger appears in the monitoring image, the video of the stranger is recorded and sent to the user, and therefore the loss of life and property of the user is avoided. In addition, the video of the stranger is recorded and sent to the user only when the stranger appears in the monitoring image. When the acquaintance appears in the monitoring image, the corresponding video can not be recorded, and the probability of misinformation of the monitoring equipment caused by the acquaintance appearing in the monitoring range is reduced.
Description
Technical Field
The present application relates to the field of monitoring technologies, and in particular, to a monitoring method, an apparatus, a storage medium, and an electronic device.
Background
Now, indoor theft or other indoor activities still occur occasionally. Lawbreakers often use night time or nobody in the room to enter the room for theft or other illegal activities. The existing security system can only check the situation of the accident through the monitoring equipment after the accident, and at the moment, lawless persons can escape. When illegal behaviors occur, the users cannot be informed in time, and therefore great threats are brought to lives and properties of people.
Disclosure of Invention
In order to solve the above problem, embodiments of the present application provide a monitoring method, a monitoring device, and an electronic device.
In a first aspect, an embodiment of the present application provides a monitoring method, including the following steps:
collecting a monitoring image;
when detecting that the monitoring image comprises a human body, identifying whether the identity of the human body is a stranger;
if yes, recording the monitoring video of the stranger according to preset time length, and sending the monitoring video and the alarm prompt information to the user terminal.
Optionally, the identifying whether the identity of the human body is a stranger includes:
calculating the similarity between the acquaintance template and the human body according to a preset acquaintance template library;
and when the similarity is smaller than a similarity threshold value, determining that the identity of the human body is a stranger.
Optionally, the identifying whether the identity of the human body is a stranger includes:
extracting the characteristic information of the human body; wherein the feature information comprises one or more of face information, posture information, hair style information, height information and dressing information;
processing the characteristic information based on a character recognition model to obtain a character recognition result;
and judging whether the identity of the human body is a stranger or not according to the person identification result.
Optionally, the method further comprises:
acquiring a human body training sample set; the human body training sample set comprises a plurality of acquaintance training samples and a plurality of stranger training samples, wherein the acquaintance training samples are associated with acquaintance labels, and the stranger training samples are associated with stranger labels;
training the human body training sample set based on a machine learning algorithm to obtain the character recognition model.
Optionally, after the monitoring video and the alarm prompt message are sent to the user terminal, the method further includes:
receiving label information from the user terminal; wherein the label indicating the human body is an acquaintance label;
marking the human body as an acquaintance label based on the marking information, and updating the human body training sample set by taking the human body as a sample;
and correcting the character recognition model based on the updated human body training sample.
Optionally, before the acquiring the monitoring image, the method further comprises:
acquiring a shooting range picture of a camera;
receiving a monitoring area checking instruction sent by a user based on the shooting range picture;
and determining a monitoring area based on the monitoring area checking instruction.
Optionally, the recording the monitoring video of the stranger according to a preset time length includes:
determining the motion trend of the human body;
determining the rotation direction and the rotation angle of the camera according to the motion trend;
and controlling the rotation of the camera through the rotation direction and the rotation angle so as to record the monitoring video of the stranger according to the preset time length.
In a second aspect, an embodiment of the present application provides a monitoring apparatus, including:
the acquisition unit is used for acquiring monitoring images;
the identification unit is used for identifying whether the identity of the human body is a stranger or not when the monitored image is detected to contain the human body;
and the sending unit is used for recording the monitoring video of the stranger according to the preset time length and sending the monitoring video and the alarm prompt information to the user terminal if the monitoring video is the stranger.
In a third aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of any one of the above methods.
In a fourth aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of any one of the above methods when executing the program.
The monitoring method, the monitoring device, the storage medium and the electronic equipment collect monitoring images; when detecting that the monitoring image comprises a human body, identifying whether the identity of the human body is a stranger; if yes, recording the monitoring video of the stranger according to preset time length, and sending the monitoring video and the alarm prompt information to the user terminal. According to the technical scheme, under the condition that a stranger appears in the monitored image, the video of the stranger is recorded and sent to the user, the user can judge whether security protection problems occur or not according to the video and carry out corresponding treatment, and therefore loss of life and property of the user is avoided. In addition, the video of the stranger is recorded and sent to the user only when the stranger appears in the monitoring image. When the acquaintance appears in the monitoring image, the corresponding video can not be recorded, and the probability of misinformation of the monitoring equipment caused by the acquaintance appearing in the monitoring range is reduced.
Drawings
Fig. 1 is a schematic diagram of an exemplary system architecture to which a monitoring method or apparatus according to an embodiment of the present application may be applied;
fig. 2 is a schematic flow chart of a monitoring method according to an embodiment of the present application;
FIG. 3 is a flowchart illustrating a method for determining a character recognition model according to an embodiment of the present application;
fig. 4 is a schematic flowchart of another monitoring method provided in the embodiment of the present application;
fig. 5 is a schematic flow chart of another monitoring method provided in the embodiments of the present application;
fig. 6 is a schematic structural diagram of a monitoring device according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The present application is further described with reference to the following figures and examples.
In the following description, the terms "first" and "second" are used for descriptive purposes only and are not intended to indicate or imply relative importance. The following description provides embodiments of the present application, where different embodiments may be substituted or combined, and thus the present application is intended to include all possible combinations of the same and/or different embodiments described. Thus, if one embodiment includes feature A, B, C and another embodiment includes feature B, D, then this application should also be considered to include an embodiment that includes one or more of all other possible combinations of A, B, C, D, even though this embodiment may not be explicitly recited in text below.
The following description provides examples, and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made in the function and arrangement of elements described without departing from the scope of the disclosure. Various examples may omit, substitute, or add various procedures or components as appropriate. For example, the described methods may be performed in an order different than the order described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.
Fig. 1 is a schematic diagram of an exemplary system architecture to which a monitoring method or apparatus according to an embodiment of the present application may be applied. The image pickup apparatus 101 is an apparatus capable of image capturing and video recording, such as a camera. The image pickup apparatus 101 may be provided inside the server 102, and the image pickup apparatus 101 may be independent of the server 102 and connected to the server 102. The connection mode includes direct connection through a data line and also includes connection through a network. The network may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The terminal device 103 is a terminal device used by a user having a management authority to the system. The user can check the real-time condition in the room through the terminal device 103, and receive stranger videos, alarm prompt information and the like sent by the camera device. The terminal device 103 includes, but is not limited to, devices such as a server, a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle-mounted terminal (e.g., a car navigation terminal), a digital television, a desktop computer, and the like.
The method comprises the steps that the camera device 101 collects a monitoring image, and the server 102 identifies whether the identity of a human body is a stranger or not when detecting that the monitoring image comprises the human body; if yes, recording the monitoring video of the stranger according to a preset time length, and sending the monitoring video and the alarm prompt information to the terminal equipment 103. The user can timely know the situation of the monitored area through the terminal device 103, if the monitored area is illegal, the situation can be timely processed, and the user is prevented from suffering great loss.
Referring to fig. 2, fig. 2 is a schematic flowchart of a monitoring method provided in an embodiment of the present application, where the method includes:
s201, collecting a monitoring image.
S202, when the monitored image comprises the human body, identifying whether the identity of the human body is a stranger.
A static monitoring picture can be stored in the system as a background picture, the captured monitoring picture is compared with the background picture to find out different image areas, and then the specific forms of the different image areas are further judged. Or intercepting the previous frame image and the next frame image of the monitoring video stream for comparison, and further judging the specific forms of the different image areas. The edge contour extraction processing can be performed on different image areas to determine object objects corresponding to different images. The above methods are well established in the prior art and are not described in detail.
And judging whether the moving object is a human body or not under the condition that the moving object is detected in the monitored image. If the moving object is an animal, a sweeping robot, or the like, the process returns to step S201. If the moving object is a human body, further judging whether the human body is an acquaintance. If the moving object is an acquaintance, the process returns to step S201. If the moving object is a stranger, the following step S203 is executed.
Optionally, the identifying whether the identity of the human body is a stranger includes:
and calculating the similarity between the acquaintance template and the human body according to a preset acquaintance template library.
And when the similarity is smaller than a similarity threshold value, determining that the identity of the human body is a stranger.
The system extracts human body characteristic information of each acquaintance based on the image of the acquaintance, wherein the characteristic information comprises one or more of face information, posture information, hair style information, height information and dressing information. The system prestores the association relationship between the characteristic information and the corresponding acquaintances into the system. That is, the system assigns an independent identifier to each acquaintance, and the identifier corresponds to a group of characteristic information of the acquaintance. The characteristic information of the multiple groups of acquaintances forms an acquaintance template library.
When the similarity between the acquaintance template and the human body in the monitoring image is calculated, the characteristic information of the human body in the monitoring image is extracted, and then the similarity between the human body characteristics in the monitoring image and the characteristic information of a plurality of groups of acquaintances prestored in the system is calculated one by one. And if the similarity between the human body features in the monitoring image and the feature information of all acquaintances prestored in the system is smaller than a set threshold value, judging that the human body in the monitoring image is a stranger. And if the similarity between the human body characteristics in the monitoring image and the characteristic information of a group of acquaintances prestored in the system is greater than a set threshold value, judging that the human body in the monitoring image is an acquaintance. The similarity threshold can be set according to actual conditions and user requirements.
Optionally, the identifying whether the identity of the human body is a stranger includes:
and extracting the characteristic information of the human body. Wherein the feature information comprises one or more of face information, posture information, hair style information, height information and dressing information.
And processing the characteristic information based on the character recognition model to obtain a character recognition result.
And judging whether the identity of the human body is a stranger or not according to the person identification result.
The embodiment of the application can also judge whether the human body in the monitored image is a stranger through the character recognition model. And extracting the characteristic information of the human body in the monitoring image, inputting the characteristic information into a human body recognition model as a characteristic vector, and judging whether the human body in the monitoring image is a stranger or not according to the output result of the human body recognition model.
And S203, if so, recording the monitoring video of the stranger according to preset time, and sending the monitoring video and the alarm prompt information to the user terminal.
The preset time length can be set by a system according to actual requirements or set by a user. The preset time period may be 10 seconds, 30 seconds, 1 minute, etc.
The user terminal may give an alarm to the user in the form of vibration, ring, text message, warning pattern, etc., for example, the user terminal may give an alarm to the user by sending a buzzer sound, etc., through the color change of the indicator light.
According to the monitoring method provided by the embodiment of the application, under the condition that a stranger appears in the monitoring image, the system automatically identifies the stranger and shoots the video of the stranger. The system sends the video of the stranger and the alarm prompt information to the user terminal. The user can calculate and know the abnormal conditions in the monitoring area from the user terminal, and the user is prevented from being damaged more. Therefore, the method provided by the embodiment of the application can solve the problem that the existing security system cannot inform the user in time when the illegal phenomenon occurs, so that great damage is caused.
The embodiment of the application also provides a method for determining the character recognition model. Fig. 3 is a flowchart illustrating a method for determining a character recognition model according to an embodiment of the present application. As shown in fig. 3, the method includes:
s301, obtaining a human body training sample set. The human body training sample set comprises a plurality of acquaintance training samples and a plurality of stranger training samples, wherein the acquaintance training samples are associated with acquaintance labels, and the stranger training samples are associated with stranger labels.
The image of the acquaintance may be provided by the user in advance. Each acquaintance should include at least one image. And collecting images of a plurality of strangers. Preferably, the images of strangers may include related images of persons of different genders, different age stages, different professions.
S302, training the human body training sample set based on a machine learning algorithm to obtain the character recognition model.
The character recognition model may be constructed based on a Convolutional Neural Network (CNN). Specifically, the system extracts human body characteristic information in each human body image, wherein the characteristic information comprises one or more of face information, posture information, hair style information, height information and dressing information. The human body feature information in each human body image is used as the input of the classification model, then, the full connection layer of the classification model is used for performing full connection processing on the classification feature vectors to obtain target vectors, then, the sofimax layer of the classification model is used, the probability that the target vectors belong to each preset class is used, and finally, whether the human body in the image is a stranger or not can be judged according to the class with the maximum corresponding probability.
Optionally, after step S203, the method further comprises:
and receiving the mark information from the user terminal. Wherein the label indicating the human body is an acquaintance label.
And marking the human body as an acquaintance label based on the marking information, and updating the human body training sample set by taking the human body as a sample.
And correcting the character recognition model based on the updated human body training sample.
The system sends the video of the stranger to the user terminal under the condition that the human body in the monitoring image is judged to be the stranger. And under the condition that the system has misjudgment, the acquaintance is mistaken as a stranger, and the video of the acquaintance is sent to the user terminal. The user may mark the person in the monitored image as an acquaintance. The system updates the human body training sample set based on the acquaintance human body, and corrects the human body recognition model so as to improve the recognition accuracy of the human body recognition model.
Referring to fig. 4, fig. 4 is a schematic flow chart of another monitoring method provided in an embodiment of the present application, where the method includes:
s401, acquiring a shooting range picture of the camera.
The shooting range picture is a monitoring picture corresponding to the maximum range which can be monitored by the camera.
S402, receiving a monitoring area checking instruction sent by a user based on the shooting range picture.
The user can check out a part of the monitoring range picture as the monitoring area. For example, the user may select a monitoring area or the like in the monitoring range screen by using a mouse.
And S403, determining a monitoring area based on the monitoring area checking instruction.
And S404, collecting a monitoring image corresponding to the monitoring area.
S405, when the monitored image is detected to comprise a human body, identifying whether the identity of the human body is a stranger.
And S406, if so, determining the motion trend of the human body.
The point of the designated position of the human body can be selected, and the predetermined trend of the human body is determined according to the position change of the point of the designated position of the human body in the monitoring picture. The point of the designated position of the human body may be the top of the head, the wrist, the shoulder, the knee, the ankle, etc. of the human body.
And S407, determining the rotation direction and the rotation angle of the camera according to the motion trend.
Preferably, the camera is a rotatable camera. The camera can rotate at a proper angle according to the motion trend of the human body in the monitoring picture so as to clearly shoot the moving picture of the monitored human body. For example, when the top of the head of the human body is located at the center of the shot picture, the human body is in continuous motion, the system calculates that the top of the head of the human body moves to a certain angle towards the west, and then the system controls the camera to rotate to a corresponding angle towards the west, so that the monitored human body can be located at the position of the center of the monitored picture all the time.
And S408, controlling the rotation of the camera through the rotation direction and the rotation angle so as to record the monitoring video of the stranger according to the preset time length, and sending the monitoring video and the alarm prompt information to the user terminal.
According to the monitoring method provided by the embodiment of the invention, the camera is controlled to rotate by a corresponding angle according to the motion trend of the human body in the monitoring picture, so that the monitored human body can be always positioned at the central position of the monitoring picture. Therefore, the moving picture of the monitored human body can be shot clearly, and the probability that the monitoring human body cannot be shot by the camera due to continuous movement of the monitored human body is reduced.
The technical solution of the present application is illustrated in a specific embodiment below. The invention provides an intelligent monitoring scheme based on AI intelligent discrimination. And through an AI intelligent judgment technology, judging whether acquaintances or strangers appear in the current monitoring area intelligently. If the person is a stranger, recording the video and sending the video to a mobile phone of the user for alarming, and the user can check the person through the video; if the person is a relative or friend of the user, the user can mark the person as an acquaintance by one key on the mobile phone terminal, and the model continues to learn and train, so that the model can judge whether the acquaintance or the stranger appears in the monitoring area more intelligently and accurately. The invention solves the problems that the prior monitoring camera can not intelligently distinguish acquaintances and strangers, has high misjudgment rate and the like.
The invention mainly utilizes an AI intelligent judgment technology to judge whether a person entering a monitoring area is a stranger or not. Fig. 5 is a schematic flowchart of another monitoring method provided in the embodiment of the present application. As shown in fig. 5, in the embodiment of the present application, the method includes:
Step 2, the user marks and classifies all the pictures, and the classification standard comprises the following steps: acquaintances and strangers.
And 3, performing model learning and training by using the marked pictures, and learning and training by using the body shape characteristics of the face, the hair style, the body system height, the body shape and the like considered in the pictures and the behavior habits of clothing, actions and the like, so that the model has the function of distinguishing acquaintances and strangers.
And 4, if monitoring detects that a person exists, judging whether the person is an acquaintance or a stranger by the model according to the step 3, and if the person is a stranger, recording the video and transmitting the video to a user for alarming.
And 5, using the user as a friend or relative of the user, and enabling the user to mark the human acquaintance by one key. The system continues to train and learn the model according to the images captured from the recorded video.
The AI intelligent identification monitoring technology is an intelligent and practical monitoring scheme. According to the scheme, acquaintances and strangers can be intelligently judged through an AI algorithm, so that monitoring is more intelligent. Compared with the prior art, the intelligent AI identification model can learn according to the facial form, the hair style, the height, the body-like characteristics of the user, the behavior habits of clothing, actions and the like, and can intelligently distinguish acquaintances or strangers entering a monitoring area according to the characteristics and the behavior habits, so that the function greatly reduces the probability of misjudgment, and the intelligent AI identification model is an efficient and practical monitoring scheme; the model is in a continuous learning state, and can acquire photos of acquaintances through monitoring to carry out deep learning, so that the judgment is more accurate; if the visitor is a friend or relative known by the user, the user can mark the visitor as an acquaintance by one key, and the model can carry out deep learning on the body type characteristics and the behavior habit of the visitor according to the continuous learning function. The invention solves the problems of insufficient intelligence, high misjudgment rate and the like in the prior art, enables the monitoring to be more intelligent and efficient, and has very wide market prospect.
Fig. 2 to 5 describe the monitoring method in detail according to the embodiment of the present application. Referring to fig. 6, fig. 6 is a schematic structural diagram of a monitoring device according to an embodiment of the present application, and as shown in fig. 6, the monitoring device includes:
the acquisition unit 601 is used for acquiring monitoring images;
the identification unit 602 is configured to identify whether the identity of the human body is a stranger when the monitored image is detected to include the human body;
and the sending unit 603 is configured to record the monitoring video of the stranger according to a preset time length and send the monitoring video and the alarm prompt information to the user terminal if the monitoring video is recorded.
Optionally, the identifying unit 602 is specifically configured to:
calculating the similarity between the acquaintance template and the human body according to a preset acquaintance template library;
and when the similarity is smaller than a similarity threshold value, determining that the identity of the human body is a stranger.
Optionally, the identifying unit 602 is specifically configured to:
extracting the characteristic information of the human body; wherein the feature information comprises one or more of face information, posture information, hair style information, height information and dressing information;
processing the characteristic information based on a character recognition model to obtain a character recognition result;
and judging whether the identity of the human body is a stranger or not according to the person identification result.
Optionally, the apparatus further comprises:
an obtaining unit 604, configured to obtain a human training sample set; the human body training sample set comprises a plurality of acquaintance training samples and a plurality of stranger training samples, wherein the acquaintance training samples are associated with acquaintance labels, and the stranger training samples are associated with stranger labels;
training the human body training sample set based on a machine learning algorithm to obtain the character recognition model.
Optionally, the apparatus further comprises:
a modification unit 605, configured to receive the tag information from the user terminal; wherein the label indicating the human body is an acquaintance label;
marking the human body as an acquaintance label based on the marking information, and updating the human body training sample set by taking the human body as a sample;
and correcting the character recognition model based on the updated human body training sample.
Optionally, the apparatus further comprises:
a determining unit 606, configured to acquire a shooting range picture of a camera;
receiving a monitoring area checking instruction sent by a user based on the shooting range picture;
and determining a monitoring area based on the monitoring area checking instruction.
Optionally, the sending unit 603 is specifically configured to:
determining the motion trend of the human body;
determining the rotation direction and the rotation angle of the camera according to the motion trend;
and controlling the rotation of the camera through the rotation direction and the rotation angle so as to record the monitoring video of the stranger according to the preset time length.
It is clear to a person skilled in the art that the solution according to the embodiments of the present application can be implemented by means of software and/or hardware. The "unit" and "module" in this specification refer to software and/or hardware that can perform a specific function independently or in cooperation with other components, where the hardware may be, for example, an FPGA (Field-Programmable Gate Array), an IC (Integrated Circuit), or the like.
Each processing unit and/or module in the embodiments of the present application may be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or may be implemented by software that executes the functions described in the embodiments of the present application.
Embodiments of the present application further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the monitoring method. The computer-readable storage medium may include, but is not limited to, any type of disk including floppy disks, optical disks, DVD, CD-ROMs, microdrive, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of media or device suitable for storing instructions and/or data.
Referring to fig. 7, a schematic structural diagram of an electronic device according to an embodiment of the present application is shown, where the electronic device may be used to implement the monitoring method in the foregoing embodiment. Specifically, the method comprises the following steps:
the memory 720 may be used to store software programs and modules, and the processor 790 executes various functional applications and data processing by operating the software programs and modules stored in the memory 720. The memory 720 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the terminal device, and the like. Further, the memory 720 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory 720 may also include a memory controller to provide the processor 790 and the input unit 730 access to the memory 720.
The input unit 730 may be used to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control. In particular, input unit 730 may include a touch-sensitive surface 731 (e.g., a touch screen, a touchpad, or a touch frame). Touch-sensitive surface 731, also referred to as a touch display screen or touch pad, can collect touch operations by a user on or near touch-sensitive surface 731 (e.g., operations by a user on or near touch-sensitive surface 731 using a finger, stylus, or any other suitable object or attachment) and drive the corresponding connection device according to a predetermined program. Alternatively, the touch sensitive surface 731 may comprise two parts, a touch detection means and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, and sends the touch point coordinates to the processor 790, and can receive and execute commands sent from the processor 790. In addition, the touch-sensitive surface 731 can be implemented in a variety of types, including resistive, capacitive, infrared, and surface acoustic wave.
The display unit 740 may be used to display information input by a user or information provided to a user and various graphic user interfaces of the terminal device, which may be configured by graphics, text, icons, video, and any combination thereof. The Display unit 740 may include a Display panel 741, and optionally, the Display panel 741 may be configured in the form of an LCD (Liquid Crystal Display), an OLED (Organic Light-Emitting Diode), or the like. Further, touch-sensitive surface 731 can overlie display panel 741 such that when touch operations are detected at or near touch-sensitive surface 731, they are passed to processor 790 for determining the type of touch event, and processor 790 then provides a corresponding visual output on display panel 741 in accordance with the type of touch event. Although in FIG. 7 the touch-sensitive surface 731 and the display panel 741 are implemented as two separate components to implement input and output functions, in some embodiments the touch-sensitive surface 731 and the display panel 741 may be integrated to implement input and output functions.
The processor 790 is a control center of the terminal device, connects various parts of the entire terminal device using various interfaces and lines, and performs various functions of the terminal device and processes data by operating or executing software programs and/or modules stored in the memory 720 and calling data stored in the memory 720, thereby integrally monitoring the terminal device. Optionally, the processor 790 may include one or more processing cores; the processor 790 may integrate an application processor, which primarily handles operating systems, user interfaces, applications, etc., and a modem processor, which primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into processor 790.
Specifically, in this embodiment, the display unit of the terminal device is a touch screen display, the terminal device further includes a memory and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include steps for implementing the monitoring method.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
All functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A method of monitoring, the method comprising:
collecting a monitoring image;
when detecting that the monitoring image comprises a human body, identifying whether the identity of the human body is a stranger;
if yes, recording the monitoring video of the stranger according to preset time length, and sending the monitoring video and the alarm prompt information to the user terminal.
2. The method of claim 1, wherein the identifying whether the identity of the human body is a stranger comprises:
calculating the similarity between the acquaintance template and the human body according to a preset acquaintance template library;
and when the similarity is smaller than a similarity threshold value, determining that the identity of the human body is a stranger.
3. The method of claim 1, wherein the identifying whether the identity of the human body is a stranger comprises:
extracting the characteristic information of the human body; wherein the feature information comprises one or more of face information, posture information, hair style information, height information and dressing information;
processing the characteristic information based on a character recognition model to obtain a character recognition result;
and judging whether the identity of the human body is a stranger or not according to the person identification result.
4. The method of claim 3, further comprising:
acquiring a human body training sample set; the human body training sample set comprises a plurality of acquaintance training samples and a plurality of stranger training samples, wherein the acquaintance training samples are associated with acquaintance labels, and the stranger training samples are associated with stranger labels;
training the human body training sample set based on a machine learning algorithm to obtain the character recognition model.
5. The method of claim 1, wherein after the transmitting the monitoring video and the alarm prompt information to the user terminal, the method further comprises:
receiving label information from the user terminal; wherein the label indicating the human body is an acquaintance label;
marking the human body as an acquaintance label based on the marking information, and updating the human body training sample set by taking the human body as a sample;
and correcting the character recognition model based on the updated human body training sample.
6. The method of claim 1, wherein prior to said acquiring a monitoring image, the method further comprises:
acquiring a shooting range picture of a camera;
receiving a monitoring area checking instruction sent by a user based on the shooting range picture;
and determining a monitoring area based on the monitoring area checking instruction.
7. The method of claim 1, wherein the recording the monitoring video of the stranger according to a preset time period comprises:
determining the motion trend of the human body;
determining the rotation direction and the rotation angle of the camera according to the motion trend;
and controlling the rotation of the camera through the rotation direction and the rotation angle so as to record the monitoring video of the stranger according to the preset time length.
8. A monitoring device, characterized in that the device comprises:
the acquisition unit is used for acquiring monitoring images;
the identification unit is used for identifying whether the identity of the human body is a stranger or not when the monitored image is detected to contain the human body;
and the sending unit is used for recording the monitoring video of the stranger according to the preset time length and sending the monitoring video and the alarm prompt information to the user terminal if the monitoring video is the stranger.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1-7 are implemented when the program is executed by the processor.
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| CN201911217251.4A CN112911204A (en) | 2019-12-03 | 2019-12-03 | Monitoring method, monitoring device, storage medium and electronic equipment |
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| CN201911217251.4A CN112911204A (en) | 2019-12-03 | 2019-12-03 | Monitoring method, monitoring device, storage medium and electronic equipment |
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