CN110569770A - Human body intrusion behavior recognition method and device, storage medium and electronic equipment - Google Patents
Human body intrusion behavior recognition method and device, storage medium and electronic equipment Download PDFInfo
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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
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
- G06V20/42—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items of sport video content
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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
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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
- G06V20/44—Event detection
Abstract
The invention provides a human body intrusion behavior identification method, a human body intrusion behavior identification device, a storage medium and electronic equipment. The human body intrusion behavior identification method comprises the following steps: acquiring a target area and real-time image information within a preset peripheral range of the target area, wherein the real-time image information comprises multiple frames of pictures; sequentially identifying each frame of picture of the real-time image information to judge whether a human body appears in the preset peripheral range; when a human body appears in the preset peripheral range, the human body is listed as a target human body and the moving track of the target human body is captured; and when the moving track of the target human body passes through the boundary line between the target area and the preset peripheral range and enters the target area, judging that the target human body has an intrusion behavior. The invention can improve the accuracy of judgment and avoid the occurrence of misjudgment.
Description
Technical Field
The application relates to the field of security protection, in particular to a human body intrusion behavior identification method and device, a storage medium and electronic equipment.
Background
With the development of the social economy, the difference between poverty and wealth is increased, more and more actions such as stealing, suicide and the like are performed, meanwhile, the tendency of the population mobility of the social economy development is increased, the traveling situations such as traveling are more and more, but in view of the fact that the Chinese historical national celebration population is different in quality, and disasters caused by misruns of dangers and forbidden areas are rare.
At present, although the first-line monitoring manufacturers in China all propose related solutions for security intrusion, for example, the algorithm application of contrast tracking of moving objects is realized by a frame-by-frame contrast technology realized by an opencv technology; for example, the recognition algorithm application that feature tracking is performed on a moving object by frame-by-frame comparison supporting a common camera, and then the intrusion is determined by calculating the motion trajectory of the object by adopting an optical flow algorithm, and the like. Although they can efficiently capture moving objects and can improve the accuracy rate through the invasion line, the contrast of various rainfalls, rainwater reflection and the like in the outdoor natural environment which can disturb dynamic tracking is faced, the trajectory tracking of fallen leaves, birds, floating plastic garbage and the like in practical application can be disturbed with a higher probability, even the motion trajectory accords with the invasion line rule, so that the occurrence of false alarm can not be avoided.
therefore, the prior art has defects and needs to be improved urgently.
disclosure of Invention
The application aims to provide a human body intrusion behavior identification method, a human body intrusion behavior identification device, a storage medium and electronic equipment, and has the beneficial effect of improving identification accuracy.
the embodiment of the application provides a human body intrusion behavior identification method, which comprises the following steps:
Acquiring a target area and real-time image information within a preset peripheral range of the target area, wherein the real-time image information comprises multiple frames of pictures;
Sequentially identifying each frame of picture of the real-time image information to judge whether a human body appears in the preset peripheral range;
When a human body appears in the preset peripheral range, the human body is listed as a target human body and the moving track of the target human body is captured;
And when the moving track of the target human body passes through the boundary line between the target area and the preset peripheral range and enters the target area, judging that the target human body has an intrusion behavior.
In the method for identifying human intrusion behavior of the present invention, when a human body appears in the preset peripheral range, the step of listing the human body as a target human body and capturing a movement trajectory of the target human body includes:
When a human body appears in the preset peripheral range, the human body is listed as a target human body in the picture of the current frame;
acquiring image characteristic information of the target human body in the current frame picture;
marking the target human body in the subsequent continuous preset frame pictures of the current frame according to the image characteristic information so as to obtain the position information of the target human body in each frame picture;
And capturing the moving track of the target human body according to the position information.
in the human intrusion behavior recognition method of the present invention, the step of obtaining the image feature information of the target human in the current frame picture includes:
Judging whether a moving object with the same main color as the target human body exists in a preset distance range of the preset peripheral range;
and if the image characteristic information does not exist, the image characteristic information is the main color information of the target human body.
in the human intrusion behavior recognition method of the present invention, the step of obtaining the image feature information of the target human in the current frame picture includes:
Judging whether a moving object with the same main color as the target human body exists in a preset distance range of the preset peripheral range;
if the image characteristic information exists, the image characteristic information is the main color information of the target human body, the outline characteristic information of the target human body and the physiological characteristic information of the target human body.
in the method for identifying human intrusion behavior of the present invention, the step of sequentially identifying each frame of picture of the real-time image information to determine whether a human body appears in the preset peripheral range includes:
extracting local pictures corresponding to the preset peripheral range of each frame of picture of the real-time image information to carry out frame-by-frame comparison;
when detecting that the variable appearing in the front frame and the rear frame of the local picture is larger than the threshold value, identifying the outline of the variable to judge whether the variable is a human body.
In the method for identifying human intrusion behavior of the present invention, after the step of determining that the target human body has an intrusion behavior when the movement trajectory of the target human body passes through the boundary between the target area and the preset peripheral range and enters the target area, the method further includes:
And when the intrusion behavior of the target human body is judged, storing the image characteristic information of the target human body.
A human intrusion behavior recognition device, comprising:
The device comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a target area and real-time image information in a preset peripheral range of the target area, and the real-time image information comprises multiple frames of pictures;
The first judgment module is used for sequentially identifying each frame of picture of the real-time image information so as to judge whether a human body appears in the preset peripheral range;
the capturing module is used for listing the human body as a target human body and capturing the moving track of the target human body when the human body appears in the preset peripheral range;
And the second judgment module is used for judging the invasion behavior of the target human body when the moving track of the target human body passes through the boundary line between the target area and the preset peripheral range and enters the target area.
in the human intrusion behavior recognition apparatus according to the present invention, the capturing module includes:
the first setting unit is used for listing the human body as a target human body in the picture of the current frame when the human body appears in the preset peripheral range;
The first acquisition unit is used for acquiring the image characteristic information of the target human body in the current frame picture;
The second acquisition unit is used for marking the target human body in the subsequent continuous preset frame pictures of the current frame according to the image characteristic information so as to acquire the position information of the target human body in each frame picture;
And the capturing unit is used for capturing the moving track of the target human body according to the position information.
a storage medium having stored therein a computer program which, when run on a computer, causes the computer to perform any of the methods described above.
an electronic device comprising a processor and a memory, the memory having stored therein a computer program, the processor being adapted to perform the method of any of the preceding claims by invoking the computer program stored in the memory.
according to the method, the target area and the real-time image information in the preset peripheral range of the target area are obtained, and the real-time image information comprises multiple frames of pictures; sequentially identifying each frame of picture of the real-time image information to judge whether a human body appears in the preset peripheral range; when a human body appears in the preset peripheral range, the human body is listed as a target human body and the moving track of the target human body is captured; when the moving track of the target human body passes through the boundary line between the target area and the preset peripheral range and enters the target area, judging that the target human body has an intrusion behavior; therefore, the intrusion behavior is recognized, and the recognition accuracy can be improved.
drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
Fig. 1 is a flowchart of a human intrusion behavior recognition method provided in some embodiments of the invention.
Fig. 2 is a block diagram of a human intrusion behavior recognition apparatus provided in some embodiments of the present invention.
FIG. 3 is a block diagram of an electronic device provided in some embodiments of the invention.
Detailed Description
reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative and are only for the purpose of explaining the present application and are not to be construed as limiting the present application.
In the description of the present application, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; may be mechanically connected, may be electrically connected or may be in communication with each other; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
The following disclosure provides many different embodiments or examples for implementing different features of the application. In order to simplify the disclosure of the present application, specific example components and arrangements are described below. Of course, they are merely examples and are not intended to limit the present application. Moreover, the present application may repeat reference numerals and/or letters in the various examples, such repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. In addition, examples of various specific processes and materials are provided herein, but one of ordinary skill in the art may recognize applications of other processes and/or use of other materials.
the terms "first," "second," "third," and the like in the description and in the claims of the present application and in the above-described drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the objects so described are interchangeable under appropriate circumstances. Furthermore, the terms "comprising" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, or apparatus, terminal, system comprising a list of steps is not necessarily limited to those steps or modules or elements expressly listed, and may include other steps or modules or elements not expressly listed, or inherent to such process, method, apparatus, terminal, or system.
Referring to fig. 1, fig. 1 is a flowchart of a human intrusion behavior recognition method according to some embodiments of the present invention. The method applies monitoring of the walled area. The method comprises the following steps:
S101, acquiring a target area and real-time image information in a preset peripheral range of the target area, wherein the real-time image information comprises multiple frames of pictures.
The monitoring area of the two cameras is overlapped with the shot shooting area at the boundary line of the target area and the preset peripheral range.
And S102, sequentially identifying each frame of picture of the real-time image information to judge whether a human body appears in the preset peripheral range.
Wherein, whether the human body appears in the preset peripheral range can be judged by identifying the variable frame by frame. In some embodiments, the step S102 specifically includes: s1021, extracting local pictures corresponding to the preset peripheral range of each frame of picture of the real-time image information, and comparing the local pictures one by one; and S1022, when detecting that the variable appearing in the front frame and the rear frame of the local picture is larger than a threshold value, identifying the outline of the variable to judge whether the variable is a human body. By narrowing the range of picture-by-picture comparison, efficiency can be improved and system congestion can be reduced.
S103, when the human body appears in the preset peripheral range, the human body is listed as a target human body, and the moving track of the target human body is captured.
in this step, various algorithms may be adopted to obtain the movement trajectory of the target human body. For example, by target delineation. In the present invention, step S103 specifically includes: s1031, when the preset peripheral range has the human body, listing the human body as a target human body in the picture of the current frame; s1032, acquiring image characteristic information of the target human body in the current frame picture; s1033, marking the target human body in the subsequent continuous preset frame pictures of the current frame according to the image characteristic information to obtain the position information of the target human body in each frame picture; s1034, capturing the movement track of the target human body according to the position information.
The image feature information may be main color information, contour information, or physiological feature information (e.g., height, face image, etc.) of the target human body. In some embodiments, the selection of the image characteristic information is dynamic for improved efficiency and improved accuracy.
for example, in some embodiments, this step S1032 may include: judging whether a moving object with the same main color as the target human body exists in a preset distance range of the preset peripheral range; and if the image characteristic information does not exist, the image characteristic information is the main color information of the target human body.
For example, in some embodiments, this step S1032 may include: judging whether a moving object with the same main color as the target human body exists in a preset distance range of the preset peripheral range; if the image characteristic information exists, the image characteristic information is the main color information of the target human body, the outline characteristic information of the target human body and the physiological characteristic information of the target human body.
Of course, it is understood that if the region has a high requirement on security, the face feature information may be used as the image feature information. In this case, of course, an ultra high definition camera is used as the camera.
And S104, judging that the target human body is invaded when the moving track of the target human body passes through the boundary line between the target area and the preset peripheral range and enters the target area.
in the step, when the human body is judged to have the intrusion behavior, warning information is sent to a monitor, or an alarm is sent on the spot to prevent the behavior of the intruder.
in some embodiments, after step S104, the method further includes: and when the intrusion behavior of the target human body is judged, storing the image characteristic information of the target human body. Therefore, the self-learning can be continuously carried out according to the stored image characteristic information of the target human body. Correspondingly, in step S103, when the detected human body matches the stored image feature information of the target human body, an early warning is directly sent to the administrator.
According to the method, the target area and the real-time image information in the preset peripheral range of the target area are obtained, and the real-time image information comprises multiple frames of pictures; sequentially identifying each frame of picture of the real-time image information to judge whether a human body appears in the preset peripheral range; when a human body appears in the preset peripheral range, the human body is listed as a target human body and the moving track of the target human body is captured; when the moving track of the target human body passes through the boundary line between the target area and the preset peripheral range and enters the target area, judging that the target human body has an intrusion behavior; therefore, the intrusion behavior is recognized, and the recognition accuracy can be improved.
referring to fig. 2, fig. 2 is a structural diagram of a human intrusion behavior recognition apparatus according to some embodiments of the present invention. Wherein, this human invasion action recognition device includes: the device comprises a first acquisition module 201, a first judgment module 202, a capture module 203 and a second judgment module 204.
The first obtaining module 201 is configured to obtain a target area and real-time image information within a preset peripheral range of the target area, where the real-time image information includes multiple frames of pictures; the monitoring area of the two cameras is overlapped with the shot shooting area at the boundary line of the target area and the preset peripheral range.
the first determining module 202 is configured to sequentially identify each frame of picture of the real-time image information to determine whether a human body appears in the preset peripheral range; wherein, whether the human body appears in the preset peripheral range can be judged by identifying the variable frame by frame. In some embodiments, the first determining module 202 is configured to extract a local picture corresponding to a preset peripheral range of each frame of picture of the real-time image information, and compare the local pictures frame by frame; when detecting that the variable appearing in the front frame and the rear frame of the local picture is larger than the threshold value, identifying the outline of the variable to judge whether the variable is a human body. By narrowing the range of picture-by-picture comparison, efficiency can be improved and system congestion can be reduced.
the capturing module 203 is configured to, when a human body appears in the preset peripheral range, list the human body as a target human body and capture a movement trajectory of the target human body. Wherein, in some embodiments, the capture module 203 comprises: the first setting unit is used for listing the human body as a target human body in the picture of the current frame when the human body appears in the preset peripheral range; the first acquisition unit is used for acquiring the image characteristic information of the target human body in the current frame picture; the second acquisition unit is used for marking the target human body in the subsequent continuous preset frame pictures of the current frame according to the image characteristic information so as to acquire the position information of the target human body in each frame picture; and the capturing unit is used for capturing the moving track of the target human body according to the position information.
the image feature information may be main color information, contour information, or physiological feature information (e.g., height, face image, etc.) of the target human body. In some embodiments, the selection of the image characteristic information is dynamic for improved efficiency and improved accuracy.
for example, in some embodiments, the first obtaining unit is configured to determine whether a moving object having the same main color as the target human body exists within a preset distance range of the preset peripheral range; and if the image characteristic information does not exist, the image characteristic information is the main color information of the target human body.
For example, in some embodiments, this step S1032 may include: judging whether a moving object with the same main color as the target human body exists in a preset distance range of the preset peripheral range; if the image characteristic information exists, the image characteristic information is the main color information of the target human body, the outline characteristic information of the target human body and the physiological characteristic information of the target human body.
The second determining module 204 is configured to determine that the target human body has an intrusion behavior when the moving trajectory of the target human body passes through a boundary between the target area and the preset peripheral range and enters the target area.
According to the method, the target area and the real-time image information in the preset peripheral range of the target area are obtained, and the real-time image information comprises multiple frames of pictures; sequentially identifying each frame of picture of the real-time image information to judge whether a human body appears in the preset peripheral range; when a human body appears in the preset peripheral range, the human body is listed as a target human body and the moving track of the target human body is captured; when the moving track of the target human body passes through the boundary line between the target area and the preset peripheral range and enters the target area, judging that the target human body has an intrusion behavior; therefore, the intrusion behavior is recognized, and the recognition accuracy can be improved.
An embodiment of the present application further provides a storage medium, where a computer program is stored in the storage medium, and when the computer program runs on a computer, the computer executes the facial expression decorating method based on speech recognition according to any of the above embodiments, so as to implement the following functions: acquiring a target area and real-time image information within a preset peripheral range of the target area, wherein the real-time image information comprises multiple frames of pictures; sequentially identifying each frame of picture of the real-time image information to judge whether a human body appears in the preset peripheral range; when a human body appears in the preset peripheral range, the human body is listed as a target human body and the moving track of the target human body is captured; and when the moving track of the target human body passes through the boundary line between the target area and the preset peripheral range and enters the target area, judging that the target human body has an intrusion behavior.
referring to fig. 3, an embodiment of the present application further provides an electronic device. The electronic device can be a smart phone, a tablet computer and the like. As shown, the electronic device 300 includes a processor 301 and a memory 302. The processor 301 is electrically connected to the memory 302. The processor 301 is a control center of the terminal 300, connects various parts of the entire terminal using various interfaces and lines, and performs various functions of the terminal and processes data by running or calling a computer program stored in the memory 302 and calling data stored in the memory 302, thereby performing overall monitoring of the terminal.
In this embodiment, the processor 301 in the electronic device 300 loads instructions corresponding to one or more processes of the computer program into the memory 302 according to the following steps, and the processor 301 runs the computer program stored in the memory 302, so as to implement various functions: acquiring a target area and real-time image information within a preset peripheral range of the target area, wherein the real-time image information comprises multiple frames of pictures; sequentially identifying each frame of picture of the real-time image information to judge whether a human body appears in the preset peripheral range; when a human body appears in the preset peripheral range, the human body is listed as a target human body and the moving track of the target human body is captured; and when the moving track of the target human body passes through the boundary line between the target area and the preset peripheral range and enters the target area, judging that the target human body has an intrusion behavior.
Memory 302 may be used to store computer programs and data. The memory 302 stores computer programs containing instructions executable in the processor. The computer program may constitute various functional modules. The processor 301 executes various functional applications and data processing by calling a computer program stored in the memory 302.
According to the method, the target area and the real-time image information in the preset peripheral range of the target area are obtained, and the real-time image information comprises multiple frames of pictures; sequentially identifying each frame of picture of the real-time image information to judge whether a human body appears in the preset peripheral range; when a human body appears in the preset peripheral range, the human body is listed as a target human body and the moving track of the target human body is captured; when the moving track of the target human body passes through the boundary line between the target area and the preset peripheral range and enters the target area, judging that the target human body has an intrusion behavior; therefore, the intrusion behavior is recognized, and the recognition accuracy can be improved.
it should be noted that, those skilled in the art can understand that all or part of the steps in the methods of the above embodiments can be implemented by hardware related to instructions of a program, and the program can be stored in a computer readable storage medium, which can include but is not limited to: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
the human intrusion behavior recognition method, the human intrusion behavior recognition device, the storage medium and the electronic device provided by the embodiments of the present application are described in detail above, and a specific example is applied in the present application to explain the principle and the implementation of the present application, and the description of the above embodiments is only used to help understanding the method and the core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. a human intrusion behavior recognition method is characterized by comprising the following steps:
acquiring a target area and real-time image information within a preset peripheral range of the target area, wherein the real-time image information comprises multiple frames of pictures;
Sequentially identifying each frame of picture of the real-time image information to judge whether a human body appears in the preset peripheral range;
When a human body appears in the preset peripheral range, the human body is listed as a target human body and the moving track of the target human body is captured;
And when the moving track of the target human body passes through the boundary line between the target area and the preset peripheral range and enters the target area, judging that the target human body has an intrusion behavior.
2. The human intrusion behavior recognition method according to claim 1, wherein the step of listing the human body as a target human body and capturing a movement trajectory of the target human body when the human body appears within the preset peripheral range comprises:
when a human body appears in the preset peripheral range, the human body is listed as a target human body in the picture of the current frame;
Acquiring image characteristic information of the target human body in the current frame picture;
Marking the target human body in the subsequent continuous preset frame pictures of the current frame according to the image characteristic information so as to obtain the position information of the target human body in each frame picture;
And capturing the moving track of the target human body according to the position information.
3. the human intrusion behavior recognition method according to claim 2, wherein the step of obtaining the image feature information of the target human in the current frame picture comprises:
Judging whether a moving object with the same main color as the target human body exists in a preset distance range of the preset peripheral range;
and if the image characteristic information does not exist, the image characteristic information is the main color information of the target human body.
4. the human intrusion behavior recognition method according to claim 2, wherein the step of obtaining the image feature information of the target human in the current frame picture comprises:
judging whether a moving object with the same main color as the target human body exists in a preset distance range of the preset peripheral range;
If the image characteristic information exists, the image characteristic information is the main color information of the target human body, the outline characteristic information of the target human body and the physiological characteristic information of the target human body.
5. The human intrusion behavior recognition method according to claim 1, wherein the step of sequentially recognizing each frame of picture of the real-time image information to determine whether a human body appears within the preset peripheral range comprises:
Extracting local pictures corresponding to the preset peripheral range of each frame of picture of the real-time image information to carry out frame-by-frame comparison;
When detecting that the variable appearing in the front frame and the rear frame of the local picture is larger than the threshold value, identifying the outline of the variable to judge whether the variable is a human body.
6. The human intrusion behavior recognition method according to claim 2, further comprising, after the step of determining that the target human body has an intrusion behavior when the movement trajectory of the target human body passes through a boundary line between the target area and the preset peripheral range and enters the target area:
and when the intrusion behavior of the target human body is judged, storing the image characteristic information of the target human body.
7. A human intrusion behavior recognition apparatus, comprising:
the device comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a target area and real-time image information in a preset peripheral range of the target area, and the real-time image information comprises multiple frames of pictures;
The first judgment module is used for sequentially identifying each frame of picture of the real-time image information so as to judge whether a human body appears in the preset peripheral range;
The capturing module is used for listing the human body as a target human body and capturing the moving track of the target human body when the human body appears in the preset peripheral range;
And the second judgment module is used for judging the invasion behavior of the target human body when the moving track of the target human body passes through the boundary line between the target area and the preset peripheral range and enters the target area.
8. The human intrusion behavior recognition device according to claim 7, wherein the capturing module comprises:
The first setting unit is used for listing the human body as a target human body in the picture of the current frame when the human body appears in the preset peripheral range;
The first acquisition unit is used for acquiring the image characteristic information of the target human body in the current frame picture;
the second acquisition unit is used for marking the target human body in the subsequent continuous preset frame pictures of the current frame according to the image characteristic information so as to acquire the position information of the target human body in each frame picture;
and the capturing unit is used for capturing the moving track of the target human body according to the position information.
9. a storage medium having stored thereon a computer program which, when run on a computer, causes the computer to perform the method of any one of claims 1 to 5.
10. An electronic device, comprising a processor and a memory, the memory having stored therein a computer program, the processor being configured to execute the method of any one of claims 1 to 5 by calling the computer program stored in the memory.
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2019
- 2019-08-30 CN CN201910810946.7A patent/CN110569770A/en not_active Withdrawn
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| CN111126317A (en) * | 2019-12-26 | 2020-05-08 | 腾讯科技（深圳）有限公司 | Image processing method, device, server and storage medium |
| CN111325937A (en) * | 2020-03-07 | 2020-06-23 | 北京迈格威科技有限公司 | Method and device for detecting crossing behavior and electronic system |
| CN112258551A (en) * | 2020-03-18 | 2021-01-22 | 北京京东振世信息技术有限公司 | Article falling detection method, device, equipment and storage medium |
| CN112258551B (en) * | 2020-03-18 | 2023-09-05 | 北京京东振世信息技术有限公司 | Article drop detection method, device, equipment and storage medium |
| CN111582025A (en) * | 2020-03-31 | 2020-08-25 | 北京明略软件系统有限公司 | Moving object identification method and device and storage medium |
| CN111582025B (en) * | 2020-03-31 | 2023-11-24 | 北京明略软件系统有限公司 | Method and device for identifying moving object and storage medium |
| CN113781741A (en) * | 2021-09-15 | 2021-12-10 | 南方电网数字电网研究院有限公司 | Power out-of-range behavior warning method, device, equipment and medium based on gateway |
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Application publication date: 20191213 |