CN111160289A - Method and device for detecting accident of target user and electronic equipment - Google Patents
Method and device for detecting accident of target user and electronic equipment Download PDFInfo
- Publication number
- CN111160289A CN111160289A CN201911413757.2A CN201911413757A CN111160289A CN 111160289 A CN111160289 A CN 111160289A CN 201911413757 A CN201911413757 A CN 201911413757A CN 111160289 A CN111160289 A CN 111160289A
- Authority
- CN
- China
- Prior art keywords
- accident
- target
- target user
- user
- scene
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
Abstract
The embodiment of the invention discloses a method and a device for detecting an accident of a target user and electronic equipment, which are used for solving the problems that the existing detection method cannot determine the type of the accident, and has single function and poor reliability. The method comprises the following steps: acquiring a target image of a target user in a target scene; taking the target image of the target user as the input of an accident detection model, and outputting an accident scene type to which the target scene belongs, wherein the accident scene type is used for representing the occurrence of an accident of the target user; the accident detection model is obtained by training a plurality of sample images of a user based on types of various accident scenes, can detect various types of accidents, and is stable and reliable.
Description
Technical Field
The embodiment of the invention relates to the technical field of safety detection, in particular to a method and a device for detecting an accident of a target user and electronic equipment.
Background
More and more adults choose to leave the hometown to do work in other places, so that more and more solitary old people and left-behind children appear, and accidents are easy to happen to the people. Most accidents are caused by the fact that the accidents cannot be found and handled in time. Therefore, the detection of accidents in solitary old people and left-behind children is of great importance.
The existing detection method is to collect position information, motion information and the like through a motion bracelet carried by solitary old people and left-behind children and a sensor carried by the solitary old people and the left-behind children, and judge whether accidents occur to the solitary old people and the left-behind children or not by judging whether the collected position information and motion information are abnormal or not.
However, the existing detection method can only judge whether accidents happen to solitary old people and left-behind children, and can not determine which accidents happen, and the existing detection method is single in function and poor in reliability.
Disclosure of Invention
The embodiment of the invention provides a method and a device for detecting an accident of a target user and electronic equipment, which are used for solving the problems that the existing detection method cannot determine the type of the accident, and has single function and poor reliability.
The embodiment of the invention adopts the following technical scheme:
in a first aspect, a method for detecting accidents for a target user is provided, the method comprising:
acquiring a target image of a target user in a target scene;
taking the target image of the target user as the input of an accident detection model, and outputting an accident scene type to which the target scene belongs, wherein the accident scene type is used for representing the occurrence of an accident of the target user;
the accident detection model is obtained by training a plurality of sample images of a user based on the types of various accident scenes.
In a second aspect, an electronic device is provided, the electronic device comprising:
the first acquisition module is used for acquiring a target image of a target user in a target scene;
the output module is used for taking the target image of the target user as the input of an accident detection model and outputting an accident scene type to which the target scene belongs, wherein the accident scene type is used for representing the occurrence of an accident of the target user;
the accident detection model is obtained by training a plurality of sample images of a user based on the types of various accident scenes.
In a third aspect, an electronic device is provided, including: a memory storing computer program instructions;
a processor which, when executed by said processor, implements the method of detecting an incident to a target user as described above.
In a fourth aspect, a computer-readable storage medium is provided, which comprises instructions that, when executed on a computer, cause the computer to carry out the method of detecting accidents for a target user as described above.
The embodiment of the invention adopts at least one technical scheme which can achieve the following beneficial effects:
according to the method for detecting the accident of the target user, provided by the embodiment of the invention, the target image of the target user in the target scene is obtained; taking the target image of the target user as the input of an accident detection model, and outputting an accident scene type to which the target scene belongs, wherein the accident scene type is used for representing the occurrence of an accident of the target user; the accident detection model is obtained by training a plurality of sample images of a user based on types of various accident scenes, can detect various types of accidents, and is stable and reliable.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a flowchart illustrating a method for detecting an accident of a target user according to an embodiment of the present disclosure;
fig. 2 is a schematic view of an actual application scenario of a detection method for an accident of a target user according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a detection apparatus for an accident of a target user according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clear, the technical solutions of the present application will be clearly and completely described below with reference to the specific embodiments of the present specification and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments in the present specification without any inventive step are within the scope of the present application.
The embodiment of the invention provides a method for detecting an accident of a target user and electronic equipment, and aims to solve the problems that the existing detection method cannot determine the type of the accident, and has single function and poor reliability. Embodiments of the present invention provide a method for detecting an accident of a target user, and an execution subject of the method may be, but is not limited to, an application program, an electronic device, or an apparatus or system capable of being configured to execute the method provided by embodiments of the present invention.
For convenience of description, the following description will be made on embodiments of the method, taking an execution subject of the method as an example of an electronic device capable of executing the method. It is to be understood that the implementation of the method as an electronic device is merely an exemplary illustration and should not be construed as a limitation of the method.
Fig. 1 is a flowchart of a method for detecting an accident of a target user according to an embodiment of the present invention, where the method of fig. 1 may be performed by an electronic device, as shown in fig. 1, and the method may include:
The target user may refer to a specific user, such as an elderly person in solitary life, a child left behind, a disabled person, or the like. Of course, the target user may also refer to a general user, such as a healthy person.
The target image of the target user may be acquired by an image acquisition device, which may be a camera. The image acquisition equipment can acquire images in real time and can also acquire images according to a preset time interval.
The step may be specifically implemented such that the electronic device receives, in real time or at preset time intervals, the target image of the target user acquired by the image acquisition device.
And 102, taking the target image of the target user as the input of an accident detection model, and outputting an accident scene type to which the target scene belongs, wherein the accident scene type is used for representing the occurrence of an accident of the target user.
The accident detection model is obtained by training a plurality of sample images of a user based on the types of various accident scenes.
The training of the accident detection model can be specifically realized by acquiring a plurality of sample images of the user in various accident scenes; and obtaining an accident detection model through neural network learning training based on the types of the various accident scenes and the plurality of sample images of the user in the various accident scenes, so as to detect the accident of the user and the type of the accident through the accident detection model.
Wherein, the neural network can adopt Tensorflow neural network, TensorflowTMThe symbolic mathematical system is a symbolic mathematical system based on data flow programming (dataflow programming), and is widely applied to programming realization of various machine learning (machine learning) algorithms.
For example, assuming that a user has a falling accident, a plurality of falling sample images corresponding to a falling scene are selected, and when the accident detection model is trained, the falling accident detection model is obtained by learning and training the plurality of falling sample images through a neural network.
Exemplarily, assuming that a syncope accident occurs to a user, a plurality of syncope sample images corresponding to the syncope scene are selected, and when the accident detection model is trained, the syncope accident detection model is obtained by learning and training the plurality of syncope sample images through a neural network.
Illustratively, assuming that a user has a falling accident and a syncope accident, a plurality of falling sample images corresponding to a falling scene and a plurality of syncope sample images corresponding to a syncope scene are selected, and when the accident detection model is trained, the plurality of falling sample images and the plurality of syncope sample images are trained through neural network learning to obtain a combined model for detecting the falling accident and the syncope accident.
According to the method for detecting the accident of the target user, provided by the embodiment of the invention, the target image of the target user in the target scene is obtained; taking the target image of the target user as the input of an accident detection model, and outputting an accident scene type to which the target scene belongs, wherein the accident scene type is used for representing the occurrence of an accident of the target user; the accident detection model is obtained by training a plurality of sample images of a user based on types of various accident scenes, can detect various types of accidents, and is stable and reliable.
As an embodiment, after step 102 is executed, the method for detecting an accident of a target user according to an embodiment of the present invention may further include:
and pushing information corresponding to the detection result of the accident of the target user to the associated user of the target user.
The associated user of the target user may refer to a person associated with the target user.
For example, if the target user is a left-behind child, the associated user may be a guardian of the left-behind child; if the target user is a disabled person, the associated user can be a spouse or an immediate relative of the disabled person; if the target user is solitary old man, the associated user may be a caregiver or a relative of solitary old man.
The information corresponding to the detection result of the target user having the accident may refer to information corresponding to the detection result, and the detection result is the detection result of the target user having the accident.
The information corresponding to the detection result may be alarm information, reminding information of an accident, and the like.
For example, assuming that the target user is solitary old people and the associated user is a caregiver, when the electronic device detects that the solitary old people has a syncope accident, the electronic device sends alarm information of the syncope accident of the solitary old people to the caregiver of the solitary old people.
During specific implementation, the electronic equipment can send alarm information to terminal equipment of an associated user through the 4G communication module, the 4G module is used, networking and butt joint of a cloud server are not needed, short messages are locally and directly sent to carry out message pushing, and the message pushing is more stable and reliable.
According to the embodiment of the invention, the information corresponding to the detection result of the accident of the target user is pushed to the associated user of the target user, and when the accident occurs, the associated user can be informed in time, so that the associated user can find and process the accident in time, and the damage caused by the accident is reduced.
As an embodiment, after step 101 is executed, the method for detecting an accident of a target user according to an embodiment of the present invention may further include:
carrying out binarization processing on the target image of the target user to obtain a binarized image;
step 102 may be specifically implemented as:
and taking the binary image as the input of an accident detection model, and outputting an accident scene type to which the target scene belongs, wherein the accident scene type is used for representing the occurrence of an accident of a target user.
Illustratively, if the target image of the target user is a color image, the color image is subjected to binarization processing to obtain a black-and-white image. And then, the black-and-white image is used as the input of the accident detection model, and the accident scene type to which the target scene belongs is output and used for representing the accident of the target user.
The embodiment of the invention obtains the binary image by carrying out the binary processing on the target image of the target user, so that the accident detection model is more convenient to process, and the processing difficulty of the accident detection model on the image is simplified.
The method of the embodiments of the present invention will be further described with reference to specific embodiments.
Fig. 2 is a flowchart illustrating a method for detecting an accident of a target user according to an embodiment of the present invention in an actual application scenario;
specifically, as shown in FIG. 2, at 201, a target image of a target user in a target scene is acquired.
At 202, a target image of the target user is subjected to binarization processing to obtain a binarized image.
At 203, the binary image is used as the input of an accident detection model, and the detection result of whether the target user has an accident or not is output; if the detection result is that an accident occurs and the accident scene type to which the target scene belongs is used for representing that the accident occurs to the target user, executing step 204; if the detection result is that no accident occurs, step 201 is executed.
At 204, information corresponding to the detection result of the accident of the target user is pushed to the associated user of the target user.
According to the method for detecting the accident of the target user, provided by the embodiment of the invention, the target image of the target user in the target scene is obtained; taking the target image of the target user as the input of an accident detection model, and outputting an accident scene type to which the target scene belongs, wherein the accident scene type is used for representing the occurrence of an accident of the target user; the accident detection model is obtained by training a plurality of sample images of a user based on types of various accident scenes, can detect various types of accidents, and is stable and reliable.
The method for detecting an accident to a target user according to the embodiment of the present specification is described in detail above with reference to fig. 1 to 2, and the apparatus for detecting an accident to a target user according to the embodiment of the present specification is described in detail below with reference to fig. 3.
Fig. 3 is a schematic structural diagram illustrating an apparatus for detecting an accident of a target user according to an embodiment of the present disclosure, and as shown in fig. 3, the apparatus may include:
a first obtaining module 301, configured to obtain a target image of a target user in a target scene;
an output module 302, configured to use the target image of the target user as an input of an accident detection model, and output an accident scene type to which the target scene belongs, where the accident scene type is used to represent that the target user has an accident;
the accident detection model is obtained by training a plurality of sample images of a user based on the types of various accident scenes.
In one embodiment, the apparatus comprises:
a pushing module 303, configured to push, to a user associated with the target user, information corresponding to a detection result that the target user has an accident.
In one embodiment, the apparatus comprises:
a second obtaining module 304, configured to obtain a plurality of sample images of the user in various accident scenes;
a training module 305, configured to obtain an accident detection model through neural network learning training based on the types of the various accident scenarios and the plurality of sample images of the user in the various accident scenarios, so as to detect the occurrence of an accident for the user and the type of the accident through the accident detection model.
In one embodiment, the apparatus comprises:
a processing module 306, configured to perform binarization processing on the target image of the target user to obtain a binarized image;
the output module 302 includes:
and the output unit is used for taking the binary image as the input of an accident detection model and outputting the accident scene type to which the target scene belongs, wherein the accident scene type is used for representing the occurrence of an accident of a target user.
According to the detection device for the accident of the target user, provided by the embodiment of the invention, the target image of the target user in the target scene is obtained; taking the target image of the target user as the input of an accident detection model, and outputting an accident scene type to which the target scene belongs, wherein the accident scene type is used for representing the occurrence of an accident of the target user; the accident detection model is obtained by training a plurality of sample images of a user based on types of various accident scenes, can detect various types of accidents, and is stable and reliable.
An electronic device according to an embodiment of the invention will be described in detail below with reference to fig. 4. Referring to fig. 4, at a hardware level, the electronic device includes a processor, optionally an internal bus, a network interface, and a memory. As shown in fig. 4, the Memory may include a Memory, such as a Random-Access Memory (RAM), and may also include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware needed to implement other services.
The processor, the network interface, and the memory may be interconnected by an internal bus, which may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an extended EISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 4, but that does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory to the memory and then runs the computer program to form the association device of the resource value-added object and the resource object on the logic level. The processor executes the program stored in the memory and is specifically configured to perform the operations of the method embodiments described herein.
The method disclosed in the embodiments of fig. 1 to 3 and the method performed by the detection device for accidents of the target user can be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
The electronic device shown in fig. 4 may also execute the methods shown in fig. 1 to fig. 2, and implement the functions of the method for detecting an accident of a target user in the embodiments shown in fig. 1 to fig. 2, which are not described herein again.
Of course, besides the software implementation, the electronic device of the present application does not exclude other implementations, such as a logic device or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or a logic device.
The embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements the processes of the method embodiments, and can achieve the same technical effects, and in order to avoid repetition, the details are not repeated here. The computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method for detecting accidents for a target user, comprising:
acquiring a target image of a target user in a target scene;
taking the target image of the target user as the input of an accident detection model, and outputting an accident scene type to which the target scene belongs, wherein the accident scene type is used for representing the occurrence of an accident of the target user;
the accident detection model is obtained by training a plurality of sample images of a user based on the types of various accident scenes.
2. The method according to claim 1, wherein after outputting an accident scene type to which the target scene belongs by taking the target image of the target user as an input of an accident detection model, the accident scene type being used for representing the occurrence of an accident for the target user, the method comprises:
and pushing information corresponding to the detection result of the accident of the target user to the associated user of the target user.
3. The method according to claim 1, wherein before outputting an accident scene type to which the target scene belongs by taking the target image of the target user as an input of an accident detection model, the accident scene type being used for representing the occurrence of an accident for the target user, the method comprises:
acquiring a plurality of sample images of a user in various accident scenes;
and obtaining an accident detection model through neural network learning training based on the types of the various accident scenes and the plurality of sample images of the user in the various accident scenes, so as to detect the accident of the user and the type of the accident through the accident detection model.
4. The method of claim 1, after acquiring the target image of the target user in the target scene, comprising:
carrying out binarization processing on the target image of the target user to obtain a binarized image;
taking the target image of the target user as the input of an accident detection model, and outputting an accident scene type to which the target scene belongs, wherein the accident scene type is used for representing that the target user has an accident, and the method comprises the following steps:
and taking the binary image as the input of an accident detection model, and outputting an accident scene type to which the target scene belongs, wherein the accident scene type is used for representing the occurrence of an accident of a target user.
5. A device for detecting accidents for a target user, comprising:
the first acquisition module is used for acquiring a target image of a target user in a target scene;
the output module is used for taking the target image of the target user as the input of an accident detection model and outputting an accident scene type to which the target scene belongs, wherein the accident scene type is used for representing the occurrence of an accident of the target user;
the accident detection model is obtained by training a plurality of sample images of a user based on the types of various accident scenes.
6. The apparatus of claim 5, wherein the apparatus comprises:
and the pushing module is used for pushing information corresponding to the detection result of the accident of the target user to the associated user of the target user.
7. The apparatus of claim 5, wherein the apparatus comprises:
the second acquisition module is used for acquiring a plurality of sample images of the user in various accident scenes;
and the training module is used for obtaining an accident detection model through neural network learning training based on the types of the various accident scenes and the plurality of sample images of the user in the various accident scenes, so as to detect the accident of the user and the type of the accident through the accident detection model.
8. The apparatus of claim 5, wherein the apparatus comprises:
the processing module is used for carrying out binarization processing on the target image of the target user to obtain a binarized image;
the output module includes:
and the output unit is used for taking the binary image as the input of an accident detection model and outputting the accident scene type to which the target scene belongs, wherein the accident scene type is used for representing the occurrence of an accident of a target user.
9. An electronic device, comprising:
a memory storing computer program instructions;
processor which, when being executed by said processor, implements a method of detecting accidents for a target user according to any one of claims 1 to 4.
10. A computer-readable storage medium, characterized in that,
the computer-readable storage medium includes instructions which, when executed on a computer, cause the computer to implement the method of detecting an accident for a target user according to any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911413757.2A CN111160289A (en) | 2019-12-31 | 2019-12-31 | Method and device for detecting accident of target user and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911413757.2A CN111160289A (en) | 2019-12-31 | 2019-12-31 | Method and device for detecting accident of target user and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111160289A true CN111160289A (en) | 2020-05-15 |
Family
ID=70559950
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911413757.2A Pending CN111160289A (en) | 2019-12-31 | 2019-12-31 | Method and device for detecting accident of target user and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111160289A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112836626A (en) * | 2021-01-29 | 2021-05-25 | 北京百度网讯科技有限公司 | Accident determination method and device, model training method and device, and electronic equipment |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105678622A (en) * | 2016-01-07 | 2016-06-15 | 平安科技（深圳）有限公司 | Analysis method and system for vehicle insurance claim-settlement photos |
| CN107610091A (en) * | 2017-07-31 | 2018-01-19 | 阿里巴巴集团控股有限公司 | Vehicle insurance image processing method, device, server and system |
| CN108549862A (en) * | 2018-04-11 | 2018-09-18 | 北京航空航天大学 | Abnormal scene detection method and device |
| CN108629963A (en) * | 2017-03-24 | 2018-10-09 | 纵目科技（上海）股份有限公司 | Traffic accident report method based on convolutional neural networks and system, car-mounted terminal |
| CN110443969A (en) * | 2018-05-03 | 2019-11-12 | 中移(苏州)软件技术有限公司 | A kind of fire point detecting method, device, electronic equipment and storage medium |
| WO2019233394A1 (en) * | 2018-06-08 | 2019-12-12 | Oppo广东移动通信有限公司 | Image processing method and apparatus, storage medium and electronic device |
-
2019
- 2019-12-31 CN CN201911413757.2A patent/CN111160289A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105678622A (en) * | 2016-01-07 | 2016-06-15 | 平安科技（深圳）有限公司 | Analysis method and system for vehicle insurance claim-settlement photos |
| CN108629963A (en) * | 2017-03-24 | 2018-10-09 | 纵目科技（上海）股份有限公司 | Traffic accident report method based on convolutional neural networks and system, car-mounted terminal |
| CN107610091A (en) * | 2017-07-31 | 2018-01-19 | 阿里巴巴集团控股有限公司 | Vehicle insurance image processing method, device, server and system |
| CN108549862A (en) * | 2018-04-11 | 2018-09-18 | 北京航空航天大学 | Abnormal scene detection method and device |
| CN110443969A (en) * | 2018-05-03 | 2019-11-12 | 中移(苏州)软件技术有限公司 | A kind of fire point detecting method, device, electronic equipment and storage medium |
| WO2019233394A1 (en) * | 2018-06-08 | 2019-12-12 | Oppo广东移动通信有限公司 | Image processing method and apparatus, storage medium and electronic device |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112836626A (en) * | 2021-01-29 | 2021-05-25 | 北京百度网讯科技有限公司 | Accident determination method and device, model training method and device, and electronic equipment |
| CN112836626B (en) * | 2021-01-29 | 2023-10-27 | 北京百度网讯科技有限公司 | Accident determining method and device, model training method and device and electronic equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109190539B (en) | Face recognition method and device | |
| CN109086734B (en) | Method and device for positioning pupil image in human eye image | |
| CN110222936B (en) | Root cause positioning method and system of business scene and electronic equipment | |
| CN111104664B (en) | Risk identification method of electronic equipment and server | |
| CN111935319B (en) | Monitoring processing method and system based on vehicle terminal system and related equipment | |
| CN112016413A (en) | Method and device for detecting abnormal behaviors between objects | |
| CN111488835B (en) | Identification method and device for staff | |
| CN112329720A (en) | Face living body detection method, device and equipment | |
| CN112689132B (en) | Target object monitoring method and monitoring equipment | |
| CN111169371A (en) | Control method and device of vehicle steering lamp, electronic equipment and storage medium | |
| CN111160289A (en) | Method and device for detecting accident of target user and electronic equipment | |
| JP2020513627A (en) | Intelligent guidance method and device | |
| CN109189677B (en) | Test method and device for updating state of variable value | |
| CN110099074B (en) | Anomaly detection method and system for Internet of things equipment and electronic equipment | |
| CN113379999A (en) | Fire detection method and device, electronic equipment and storage medium | |
| CN111222592A (en) | Method and device for acquiring two-dimensional code of equipment | |
| CN111985438A (en) | Static face processing method, device and equipment | |
| CN116872957A (en) | Early warning method and device for intelligent driving vehicle, electronic equipment and storage medium | |
| CN111753662A (en) | Traffic accident handling method, equipment and medium | |
| CN111369591A (en) | Method, device and equipment for tracking moving object | |
| CN108667893B (en) | Data recommendation method and device and electronic equipment | |
| CN110648314A (en) | Method, device and equipment for identifying copied image | |
| CN111652184B (en) | Smoke identification method, device, storage medium and data processing equipment | |
| EP4148538A1 (en) | Action association method based on internet of things, and electronic device and storage medium | |
| CN114613099A (en) | Danger early warning method and device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |