CN111210592A - Video identification monitoring method, computer device and computer readable storage medium - Google Patents
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/46—Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames
- G06V20/47—Detecting features for summarising video content
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- 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
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/161—Detection; Localisation; Normalisation
- G06V40/166—Detection; Localisation; Normalisation using acquisition arrangements
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- 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
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/172—Classification, e.g. identification
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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
- G06V40/20—Movements or behaviour, e.g. gesture recognition
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
- G08B21/0407—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons based on behaviour analysis
- G08B21/043—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons based on behaviour analysis detecting an emergency event, e.g. a fall
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
- G08B21/0438—Sensor means for detecting
- G08B21/0446—Sensor means for detecting worn on the body to detect changes of posture, e.g. a fall, inclination, acceleration, gait
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
- G08B21/0438—Sensor means for detecting
- G08B21/0476—Cameras to detect unsafe condition, e.g. video cameras
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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
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Abstract
The invention provides a video identification monitoring method, a computer device and a computer readable storage medium, wherein the video identification monitoring method comprises the following steps: acquiring action data and face data of a person under guardianship in real time; and sending early warning information when the person under guardianship is confirmed to have dangerous action or negative emotion according to the action data and the face data. The computer device comprises a controller, and the controller is used for implementing the video identification monitoring method when executing a computer program stored in a memory. The computer readable storage medium has a computer program stored thereon, and the computer program is executed by the controller to implement the video identification monitoring method. The video identification monitoring method can realize intelligent identification and early warning of a monitoring system.
Description
Technical Field
The invention relates to the technical field of video monitoring, in particular to a video identification monitoring method, a computer device applying the video identification monitoring method and a computer readable storage medium applying the video identification monitoring method.
Background
At present, the face recognition technology is more and more mature, and under the development opportunity, a monitoring system based on video image recognition is developed according to the objective requirements of monitoring of special groups such as children, old people and mental patients, and is specially applied to monitoring of special groups such as children, old people and mental patients, so that nursing staff can timely manage the monitoring system, and serious problems can be avoided. However, the existing technology basically only has the function of manual monitoring conversation, and cannot be separated from personnel monitoring, and the system itself cannot perform intelligent identification and early warning.
Disclosure of Invention
The invention aims to provide a video identification monitoring method capable of realizing intelligent identification and early warning of a monitoring system.
The second objective of the present invention is to provide a computer device capable of implementing intelligent recognition and early warning of the monitoring system.
The third objective of the present invention is to provide a computer readable storage medium for implementing intelligent recognition and early warning of a monitoring system.
In order to achieve the first object, the video identification monitoring method provided by the invention comprises the following steps: acquiring action data and face data of a person under guardianship in real time; and sending early warning information when the person under guardianship is confirmed to have dangerous action or negative emotion according to the action data and the face data.
According to the scheme, the video identification monitoring method provided by the invention has the advantages that the action data and the face data of the person under guardianship are obtained in real time, and the early warning signal is sent when the dangerous action or negative emotion of the person under guardianship is judged, so that nursing personnel is warned in time, and the monitoring labor investment of the nursing personnel is reduced.
In a further scheme, when the fact that dangerous actions or negative emotions occur to the person under guardianship is confirmed according to the action data and the face data, the step of sending early warning information comprises the following steps: and judging whether the person under guardianship falls down or not according to the action data, and if so, sending fall-down early-warning information.
Therefore, whether the person under guardianship falls or not is judged by utilizing the action data, and falling early warning information is sent when the person under guardianship falls, so that nursing staff are warned to handle in time.
In a further scheme, the step of judging whether the person under guardianship falls or not according to the action data comprises the following steps: and when the chest center coordinate of the person under guardianship moves downwards at a speed greater than the preset speed, judging that the person under guardianship falls down.
Therefore, whether the person under guardianship falls down or not is judged according to the chest center coordinate, and the judgment is simpler and more convenient.
In a further scheme, when the fact that dangerous actions or negative emotions occur to the person under guardianship is confirmed according to the action data and the face data, the step of sending early warning information comprises the following steps: and judging whether the preset low falling emotion of the person under guardianship exceeds a first preset time period or not according to the face data, and if so, sending emotion early warning information.
Therefore, the emotion of the person under guardianship is recognized in real time through the face data, so that better diagnosis assistance is realized, and a nursing person can better nurse the person under guardianship.
In a further scheme, the step of judging whether the preset low emotion of the person under guardianship exceeds the preset time according to the face data comprises the following steps: obtaining expression data in the current video frame, predicting the expression data by using a preset algorithm, and obtaining the expression type in the current video frame.
Therefore, the expression data are predicted through the preset algorithm, and the accuracy of judging the expression type can be improved.
In a further aspect, after the step of acquiring the motion data and the face data of the person under guardianship in real time, the method further comprises: and carrying out identification and authentication according to the face data, and confirming that the current face is the person under guardianship.
Therefore, in order to accurately look up the person under guardianship, the person under guardianship needs to be identified and authenticated according to the face data, and only the action data and the face data of the person under guardianship are processed, so that the monitoring accuracy is improved.
In a further aspect, after performing identification and authentication according to the face data, the method further includes: and if the strange face continues for a second preset time, sending stranger intrusion early warning information.
Therefore, for the safety of the person under guardianship, if the stranger face continues for the second preset time, stranger intrusion early warning information is sent so as to remind nursing staff.
In a further aspect, before the step of acquiring motion data and face data of a person under guardianship in real time, the method further comprises: the face characteristic data of the person under guardianship is acquired, and the identity information of the person under guardianship is input.
Therefore, in order to enable the monitoring system to effectively provide monitoring services for the person under guardianship, the face characteristic data of the person under guardianship needs to be acquired, and the identity information of the person under guardianship is input so as to be convenient for authentication.
In order to achieve the second objective of the present invention, the present invention provides a computer device including a processor and a memory, wherein the memory stores a computer program, and the computer program is executed by the processor to implement the steps of the video identification monitoring method.
In order to achieve the third object of the present invention, the present invention provides a computer readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a controller, implements the steps of the video identification monitoring method described above.
Drawings
Fig. 1 is a flow chart of an embodiment of a video identification monitoring method according to the invention.
The invention is further explained with reference to the drawings and the embodiments.
Detailed Description
The video identification monitoring method is an application program applied to an intelligent terminal and used for realizing intelligent identification early warning. Preferably, the intelligent terminal is provided with a camera. The invention also provides a computer device, which comprises a controller, wherein the controller is used for implementing the steps of the video identification monitoring method when executing the computer program stored in the memory. The present invention also provides a computer readable storage medium, on which a computer program is stored, the computer program, when being executed by a controller, implements the steps of the video identification monitoring method.
The embodiment of the video identification monitoring method comprises the following steps:
the video identification monitoring method is an application program applied to an intelligent terminal and is used for realizing intelligent identification early warning.
As shown in fig. 1, when the video identification monitoring method of the present invention works, step S1 is executed first to obtain facial feature data of a person under guardianship and to record identity information of the person under guardianship. In order to enable the monitoring system to effectively provide monitoring service for the person under guardianship, the face characteristic data of the person under guardianship needs to be acquired, and the identity information of the person under guardianship is input so as to be convenient for authentication. The system can store the photo and identity information of the monitored person in a database, and the system can identify the monitored person when acquiring the face data so as to carry out authentication.
Next, step S2 is executed to acquire motion data and face data of the person under guardianship in real time. In order to monitor the person under guardianship in real time, the camera acquires the action data and the face data of the person under guardianship in real time so as to analyze the action data and the face data and confirm whether the early warning information needs to be sent or not.
After acquiring the motion data and the face data of the person under guardianship, step S3 is executed to perform identification and authentication according to the face data and confirm that the current face is the person under guardianship. In order to accurately keep a person under guardianship, identification and authentication are required to be performed according to face data. And comparing the face data with the face characteristic data of the person under guardianship stored in the system, thereby confirming whether the face in the current video frame is the person under guardianship or not.
After confirming that the current face is the person under guardianship, step S4 is executed to determine whether the person under guardianship has dangerous actions or has negative emotions according to the action data and the face data. By judging the action data and the number of faces, whether dangerous actions or negative emotions appear on the person under guardianship is confirmed, and corresponding processing can be carried out.
In this embodiment, the step of determining whether a person under guardianship has a dangerous action or a negative emotion according to the action data and the face data includes: and judging whether the person under guardianship falls down or not according to the action data. Wherein, the step of judging whether the person under guardianship falls or not according to the action data comprises the following steps: and when the chest center coordinate of the person under guardianship moves downwards at a preset speed, judging that the person under guardianship falls down. The preset rate can be set according to experimental data. In this embodiment, motion data is predicted through a human-dose-animation-0001 algorithm model based on MobileNet v1 provided by OpenVINO, and the OpenVINO algorithm model is an autonomous learning algorithm, which is a technique known by those skilled in the art and is not described herein again. When character motion detection is started, left shoulder coordinates, right shoulder coordinates and chest center coordinates are obtained, the distance between the left shoulder and the right shoulder is determined through the left shoulder coordinates and the right shoulder coordinates, and the chest center coordinates are determined by taking the distance between the left shoulder and the right shoulder as a reference distance. By judging a certain number of continuous video frames, if the center coordinates of the chest of the person under guardianship move downwards at a speed greater than a preset speed, the person under guardianship is judged to fall. For example, if the chest center coordinate in the current video frame is (0, 0) and the distance between the left shoulder and the right shoulder is 30 cm, the left shoulder coordinate is (-15, 0), and the right shoulder coordinate is (15, 0), and if the chest center coordinate in the next video frame becomes (0, -15), it means that the chest center coordinate has fallen by 15 cm, it is determined that the person under guardianship falls.
In this embodiment, the step of determining whether a person under guardianship has a dangerous action or a negative emotion according to the action data and the face data further includes: and judging whether the preset low emotion of the person under guardianship exceeds a first preset time period or not according to the face data. The first preset duration may be obtained from experimental data. The preset low emotion can be set according to needs, for example, emotion recognition is set to seven expression types in total: vital qi generation, nausea, heart injury, fright, happy feeling, flat feeling and surprise, wherein the first four are negative emotions, and the last three are positive emotions.
The step of judging whether the preset low emotion of the person under guardianship exceeds the preset time according to the face data comprises the following steps: obtaining expression data in the current video frame, predicting the expression data by using a preset algorithm, and obtaining the expression type in the current video frame. In this embodiment, the preset algorithm adopts an Xception algorithm model, which is an autonomous learning algorithm and is a technique known to those skilled in the art. The Xcaption algorithm model can train an emotion recognition model, and after the video frame is obtained, the Xcaption algorithm model can predict the emotion of the human in the current video frame. And obtaining a predicted value of each expression type through prediction, wherein the expression type of the character in the current video frame is the highest predicted value. By predicting the expression types of the continuous video frames, whether the guardian is in the preset low emotion exceeding the preset time length can be determined.
If it is judged that no dangerous action or negative emotion occurs based on the action data and the face data, the process returns to step S2 to continue acquiring the action data and the face data of the person under guardianship. If it is confirmed that the person under guardianship has a dangerous action or a negative emotion according to the action data and the face data, step S5 is executed to transmit warning information. When the system sends the early warning information, the early warning information corresponding to dangerous actions or negative emotions is sent to the nursing terminal. And sending emotion early warning information when the preset low emotion of the person under guardianship is judged to exceed the first preset time length according to the face data.
In addition, after the step S3 is executed, after the identification authentication is performed according to the face data, if a stranger face occurs for a second preset time, stranger intrusion warning information is sent. The second preset duration may be set as desired. And for the safety of the person under guardianship, if a stranger face continues for a second preset time, sending stranger intrusion early warning information so as to remind nursing staff.
The embodiment of the computer device comprises:
the computer device of this embodiment includes a controller, and the controller implements the steps of the video identification monitoring method when executing the computer program.
For example, a computer program may be partitioned into one or more modules, which are stored in a memory and executed by a controller to implement the present invention. One or more of the modules may be a sequence of computer program instruction segments for describing the execution of a computer program in a computer device that is capable of performing certain functions.
The computer device may include, but is not limited to, a controller, a memory. Those skilled in the art will appreciate that the computer apparatus may include more or fewer components, or combine certain components, or different components, e.g., the computer apparatus may also include input-output devices, network access devices, buses, etc.
For example, the controller may be a Central Processing Unit (CPU), other general purpose controller, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable Gate Array (FPGA) or other programmable logic device, discrete Gate or transistor logic, discrete hardware components, and so on. The general controller may be a microcontroller or the controller may be any conventional controller or the like. The controller is the control center of the computer device and connects the various parts of the entire computer device using various interfaces and lines.
The memory may be used to store computer programs and/or modules, and the controller may implement various functions of the computer apparatus by executing or otherwise executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. For example, the memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function (e.g., a sound receiving function, a sound-to-text function, etc.), and the like; the storage data area may store data (e.g., audio data, text data, etc.) created according to the use of the cellular phone, etc. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a flash memory Card (FlashCard), at least one magnetic disk storage device, a flash memory device, or other volatile solid state storage device.
Computer-readable storage medium embodiments:
the modules integrated by the computer apparatus of the above embodiments, if implemented in the form of software functional units and sold or used as independent products, may be stored in a computer-readable storage medium. Based on such understanding, all or part of the processes in the above embodiments of the video identification monitoring method may also be implemented by a computer program instructing related hardware to complete, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a controller, the steps in the above embodiments of the video identification monitoring method may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The storage medium may include: any entity or device capable of carrying computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), random-access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, in accordance with legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunications signals.
Therefore, the video identification monitoring method of the invention sends the early warning signal by acquiring the action data and the face data of the person under guardianship in real time and judging that the person under guardianship has dangerous action or negative emotion, thereby warning nursing staff in time and reducing the investment of nursing labor of the nursing staff.
It should be noted that the above is only a preferred embodiment of the present invention, but the design concept of the present invention is not limited thereto, and any insubstantial modifications made by using the design concept also fall within the protection scope of the present invention.
Claims (10)
1. A video identification monitoring method is characterized in that: the method comprises the following steps:
acquiring action data and face data of a person under guardianship in real time;
and sending early warning information when the person under guardianship is confirmed to have dangerous action or negative emotion according to the action data and the face data.
2. The video identification monitoring method according to claim 1, wherein:
the step of sending early warning information when the person under guardianship is confirmed to have dangerous action or negative emotion according to the action data and the face data comprises the following steps:
and judging whether the person under guardianship falls down according to the action data, and if so, sending fall-down early warning information.
3. The video identification monitoring method according to claim 2, wherein:
the step of judging whether the person under guardianship falls or not according to the action data comprises the following steps:
and when the chest center coordinate of the person under guardianship moves downwards at a speed greater than a preset speed, judging that the person under guardianship falls down.
4. The video identification monitoring method according to claim 1, wherein:
the step of sending early warning information when the person under guardianship is confirmed to have dangerous action or negative emotion according to the action data and the face data comprises the following steps:
and judging whether the preset low emotion of the person under guardianship exceeds a first preset time length or not according to the face data, and if so, sending emotion early warning information.
5. The video identification monitoring method according to claim 4, wherein:
the step of judging whether the preset low emotion of the person under guardianship exceeds the preset time according to the face data comprises the following steps:
the method comprises the steps of obtaining expression data in a current video frame, predicting the expression data by using a preset algorithm, and obtaining an expression type in the current video frame.
6. The video identification monitoring method according to any one of claims 1 to 5, wherein:
after the step of acquiring motion data and face data of a person under guardianship in real time, the method further comprises:
and carrying out identification and authentication according to the face data, and confirming that the current face is the person under guardianship.
7. The video identification monitoring method according to claim 6, wherein:
after the identification authentication is performed according to the face data, the method further comprises:
and if the strange face continues for a second preset time, sending stranger intrusion early warning information.
8. The video identification monitoring method according to any one of claims 1 to 5, wherein:
prior to the step of acquiring motion data and face data of a person under guardianship in real time, the method further comprises:
and acquiring the facial feature data of the person under guardianship, and inputting the identity information of the person under guardianship.
9. A computer device comprising a processor and a memory, wherein: the memory stores a computer program which, when being executed by the processor, carries out the steps of the method for video recognition monitoring as claimed in any one of claims 1 to 8.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program, when being executed by a controller, implements the steps of the video identification surveillance method according to any one of claims 1 to 8.
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Cited By (5)
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| CN111680607A (en) * | 2020-06-03 | 2020-09-18 | 长春博立电子科技有限公司 | Ward abnormity monitoring method and system based on video analysis |
| CN111898468A (en) * | 2020-07-09 | 2020-11-06 | 北京荟健科技有限公司 | Pregnant woman movement monitoring and alarming method and device, computer equipment and storage medium |
| CN112735083A (en) * | 2021-01-19 | 2021-04-30 | 齐鲁工业大学 | Embedded gateway for flame detection by using YOLOv5 and OpenVINO and deployment method thereof |
| CN113052146A (en) * | 2021-04-30 | 2021-06-29 | 中国银行股份有限公司 | Emotion early warning method and device |
| CN113591821A (en) * | 2021-10-08 | 2021-11-02 | 广州洛克韦陀安防科技有限公司 | Image identification security system based on big data screening |
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Application publication date: 20200529 |