CN113627330B - Method and device for identifying target type dynamic image and electronic equipment - Google Patents
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Abstract
Provided are a method, a device, an electronic device, a computer readable storage medium and a computer program product for identifying a target type dynamic image, which relate to the technical field of artificial intelligence, in particular to the technical field of computer vision. The implementation scheme is as follows: acquiring a target frame of a dynamic image, wherein the target frame is one of a plurality of image frames contained in the dynamic image; in response to determining that the target frame contains the preset biological feature, determining a degree of association of the target frame with a preset type image model, wherein the preset type image model contains the preset biological feature; determining motion information of an organism related to a preset biological feature contained in the plurality of image frames in response to determining that the association is greater than an association threshold; and determining that the dynamic image belongs to the target type dynamic image in response to determining that the motion information meets the preset condition. The technical scheme of the embodiment of the disclosure can improve the accuracy of identifying the target type dynamic image.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence, and more particularly, to the field of computer vision, and in particular, to a method, an apparatus, an electronic device, a computer readable storage medium, and a computer program product for identifying a target type moving image.
Background
Artificial intelligence is the discipline of studying the process of making a computer mimic certain mental processes and intelligent behaviors (e.g., learning, reasoning, thinking, planning, etc.) of a person, both hardware-level and software-level techniques. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing, etc.: the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, a machine learning/deep learning technology, a big data processing technology, a knowledge graph technology and the like.
The inventors of the present disclosure have appreciated that a graph, such as in gif format, may be uploaded to a network bypassing the auditing of some network information auditing platforms, thereby creating a potential for maintaining network information health.
The approaches described in this section are not necessarily approaches that have been previously conceived or pursued. Unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Similarly, the problems mentioned in this section should not be considered as having been recognized in any prior art unless otherwise indicated.
Disclosure of Invention
The present disclosure provides a method, apparatus, electronic device, computer-readable storage medium, and computer program product for identifying a target type moving image.
According to an aspect of the present disclosure, there is provided a method of identifying a target type moving image, including:
Acquiring a target frame of a dynamic image, wherein the target frame is one of a plurality of image frames contained in the dynamic image;
in response to determining that the target frame contains a preset biological feature, determining a degree of association of the target frame with a preset type image model, the preset type image model containing the preset biological feature;
Responsive to determining that the degree of association is greater than a degree of association threshold, determining motion information of an organism associated with the preset biometric feature contained in the plurality of image frames; and
And in response to determining that the motion information meets a preset condition, determining that the dynamic image belongs to a target type dynamic image.
According to another aspect of the present disclosure, there is provided an apparatus for recognizing a target type moving image, including:
an acquisition unit configured to acquire a target frame of a moving image, the target frame being one of a plurality of image frames included in the moving image;
A first determining unit configured to determine, in response to determining that the target frame contains a preset biometric feature, a degree of association of the target frame with a preset type image model, the preset type image model containing the preset biometric feature;
a second determining unit configured to determine motion information of an organism related to the preset biometric feature contained in the plurality of image frames in response to determining that the degree of association is greater than a degree of association threshold; and
And a third determination unit configured to determine that the moving image belongs to a target type moving image in response to determining that the motion information meets a preset condition.
According to still another aspect of the present disclosure, there is provided an electronic apparatus including: a memory; and a processor coupled to the memory, the processor configured to execute a method of identifying a target type moving image according to the foregoing, based on instructions stored in the memory.
According to still another aspect of the present disclosure, there is provided a computer-readable storage medium in which a computer program, when executed by a processor, implements the aforementioned method of identifying a target type moving image.
According to yet another aspect of the present disclosure, a computer program product is provided, wherein the computer program, when executed by a processor, implements the aforementioned method of identifying a target type dynamic image.
According to one or more embodiments of the present disclosure, accuracy in identifying a target type moving image may be improved, and thus, the limitation requirements of a network content platform for transmitting network information may be satisfied.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The accompanying drawings illustrate exemplary embodiments and, together with the description, serve to explain exemplary implementations of the embodiments. The illustrated embodiments are for exemplary purposes only and do not limit the scope of the claims. Throughout the drawings, identical reference numerals designate similar, but not necessarily identical, elements.
FIG. 1 illustrates a schematic diagram of an exemplary system in which various methods described herein may be implemented, in accordance with an embodiment of the present disclosure;
FIG. 2 illustrates a flow chart of a method of identifying a target type dynamic image according to some embodiments of the present disclosure;
FIG. 3 illustrates a flowchart of a method of identifying a target type dynamic image according to one embodiment of the present disclosure;
FIG. 4 illustrates a block diagram of an apparatus for recognizing a target type moving image according to some embodiments of the present disclosure; and
Fig. 5 illustrates a block diagram of an electronic device, according to some embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In the present disclosure, the use of the terms "first," "second," and the like to describe various elements is not intended to limit the positional relationship, timing relationship, or importance relationship of the elements, unless otherwise indicated, and such terms are merely used to distinguish one element from another. In some examples, a first element and a second element may refer to the same instance of the element, and in some cases, they may also refer to different instances based on the description of the context.
The terminology used in the description of the various examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, the elements may be one or more if the number of the elements is not specifically limited. Furthermore, the term "and/or" as used in this disclosure encompasses any and all possible combinations of the listed items.
The inventor of the present disclosure has appreciated that some network information auditing platforms perform content type identification on dynamic images uploaded by users in a frame extraction manner. Although the network information auditing platform does not allow users to upload dynamic images of some kind of restricted propagation, for example, gif-formatted images can often be uploaded to the network bypassing auditing, which creates a certain hidden danger to maintaining the health of the network information.
Based on the above, the embodiments of the present disclosure provide a method, an apparatus, an electronic device, a computer readable storage medium, and a computer program product for identifying a target type dynamic image, so as to improve accuracy of identifying a target type dynamic image, and the method and the apparatus can be used for over-examination of uploading contents by various network content platforms, further can meet limitation requirements of the network content platforms on transmission network information, and are beneficial to maintaining health of the network information.
Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.
Fig. 1 illustrates a schematic diagram of an exemplary system 100 in which various methods and apparatus described herein may be implemented, in accordance with an embodiment of the present disclosure. Referring to fig. 1, the system 100 includes one or more client devices 101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120. Client devices 101, 102, 103, 104, 105, and 106 may be configured to execute one or more applications.
In an embodiment of the present disclosure, the server 120 may run one or more services or software applications that enable execution of the methods of embodiments of the present disclosure.
In some embodiments, server 120 may also provide other services or software applications that may include non-virtual environments and virtual environments. In some embodiments, these services may be provided as web-based services or cloud services, for example, provided to users of client devices 101, 102, 103, 104, 105, and/or 106 under a software as a service (SaaS) model.
In the configuration shown in fig. 1, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or a combination thereof that are executable by one or more processors. A user operating client devices 101, 102, 103, 104, 105, and/or 106 may in turn utilize one or more client applications to interact with server 120 to utilize the services provided by these components. It should be appreciated that a variety of different system configurations are possible, which may differ from system 100. Accordingly, FIG. 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.
The user may use client devices 101, 102, 103, 104, 105, and/or 106 to drive a dynamic image onto the web content platform. The client device may provide an interface that enables a user of the client device to interact with the client device. The client device may also output information to the user via the interface. Although fig. 1 depicts only six client devices, those skilled in the art will appreciate that the present disclosure may support any number of client devices.
Client devices 101, 102, 103, 104, 105, and/or 106 may include various types of computer devices, such as portable handheld devices, general purpose computers (such as personal computers and laptop computers), workstation computers, wearable devices, gaming systems, thin clients, various messaging devices, sensors or other sensing devices, and the like. These computer devices may run various types and versions of software applications and operating systems, such as MICROSOFT Windows, APPLE iOS, UNIX-like operating systems, linux, or Linux-like operating systems (e.g., GOOGLE Chrome OS); or include various mobile operating systems such as MICROSOFT Windows Mobile OS, iOS, windows Phone, android. Portable handheld devices may include cellular telephones, smart phones, tablet computers, personal Digital Assistants (PDAs), and the like. Wearable devices may include head mounted displays and other devices. The gaming system may include various handheld gaming devices, internet-enabled gaming devices, and the like. The client device is capable of executing a variety of different applications, such as various Internet-related applications, communication applications (e.g., email applications), short Message Service (SMS) applications, and may use a variety of communication protocols.
Network 110 may be any type of network known to those skilled in the art that may support data communications using any of a number of available protocols, including but not limited to TCP/IP, SNA, IPX, etc. For example only, the one or more networks 110 may be a Local Area Network (LAN), an ethernet-based network, a token ring, a Wide Area Network (WAN), the internet, a virtual network, a Virtual Private Network (VPN), an intranet, an extranet, a Public Switched Telephone Network (PSTN), an infrared network, a wireless network (e.g., bluetooth, WIFI), and/or any combination of these and/or other networks.
The server 120 may include one or more general purpose computers, special purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-end servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and/or combination. The server 120 may include one or more virtual machines running a virtual operating system, or other computing architecture that involves virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain virtual storage devices of the server). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.
The computing units in server 120 may run one or more operating systems including any of the operating systems described above as well as any commercially available server operating systems. Server 120 may also run any of a variety of additional server applications and/or middle tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.
In some implementations, server 120 may include one or more applications to analyze and consolidate data feeds and/or event updates received from users of client devices 101, 102, 103, 104, 105, and 106. Server 120 may also include one or more applications to display data feeds and/or real-time events via one or more display devices of client devices 101, 102, 103, 104, 105, and 106.
In some implementations, the server 120 may be a server of a distributed system or a server that incorporates a blockchain. The server 120 may also be a cloud server, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology. The cloud server is a host product in a cloud computing service system, so as to solve the defects of large management difficulty and weak service expansibility in the traditional physical host and Virtual special server (VPS PRIVATE SERVER) service.
The system 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of databases 130 may be used to store information such as audio files and video files. The data store 130 may reside in a variety of locations. For example, the data store used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The data store 130 may be of different types. In some embodiments, the data store used by server 120 may be a database, such as a relational database. One or more of these databases may store, update, and retrieve the databases and data from the databases in response to the commands.
In some embodiments, one or more of databases 130 may also be used by applications to store application data. The databases used by the application may be different types of databases, such as key value stores, object stores, or conventional stores supported by the file system.
The system 100 of fig. 1 may be configured and operated in various ways to enable application of the various methods and apparatus described in accordance with the present disclosure.
As shown in fig. 2, some embodiments of the present disclosure provide a method 200 of identifying a target type moving image, the method including the following steps S201 to S204.
In step S201, a target frame of a moving image, which is one of a plurality of image frames included in the moving image, is acquired.
In step S202, in response to determining that the target frame contains the preset biometric feature, a degree of association of the target frame with a preset type image model is determined, the preset type image model containing the preset biometric feature.
In step S203, in response to determining that the degree of association is greater than the threshold degree of association, motion information of an organism associated with the preset biometric feature, which is included in the plurality of image frames, is determined.
In step S204, in response to determining that the motion information meets the preset condition, it is determined that the moving image belongs to the target type moving image.
The technical scheme of the embodiment of the disclosure relates to comprehensive judgment of multi-dimensional and multi-frame information, is different from simple frame extraction judgment in the related art, can effectively improve the accuracy of identifying the target type dynamic image, and can further meet the limit requirement of a network content platform on the transmission network information. The multi-dimensional judgment comprises whether the target frame contains preset biological characteristics, the association degree of the target frame and a preset type image model, whether the motion information accords with preset conditions and the like.
In the embodiment of the disclosure, the target type dynamic image includes a preset biological feature, and the preset biological feature is, for example, a human face or a certain part of a human body. When the information on the preset biometric feature in the moving image satisfies a certain condition, the moving image can be considered to belong to a target type moving image, for example, to a moving image which is restricted from being propagated on the network content platform without allowing uploading.
Taking a face as a preset biological feature as an example, the principle of the technical scheme in the embodiment of the disclosure is as follows: whether the target frame contains the human face or not can be judged through a human face feature resolution algorithm (only whether the image contains the human face or not is judged, and identity recognition is not needed). In step S202, in response to determining that the target frame contains a face, it is further necessary to determine the association degree of the target frame with the preset type image model. The preset type image model is a certain type of image model which is limited to be transmitted on the network content platform and is not allowed to be uploaded, the preset type image model comprises a human face and can be used as a reference standard for determining the association degree, and the sex of the person in the dynamic image can be judged through the step. In step S203, in response to determining that the degree of association is greater than the threshold degree of association, motion information of the human body, such as motion information of the entire human body or a certain portion of the human body, included in the plurality of image frames is determined. The association threshold may be set according to a restriction level of the uploaded information by the network content platform. Further, in step S204, in response to determining that the motion information meets a preset condition, for example, the set periodicity is presented, it is determined that the moving image belongs to the target type moving image.
In the implementation of the present disclosure, the moving image may be not only a moving image in gif format, but also a short video in format such as wmv, avi, mpg, mpeg,3gp, mov, mp4, flv, f4v, m2t, mts, rmvb, von, mkv, etc., and the playing time of the short video is 5 seconds, 10 seconds, 15 seconds, etc., for example. In one embodiment, the dynamic image is a gif-format moving picture, and the embodiment method is suitable for identifying and detecting the gif-format moving picture.
Gif pictures generally include two versions of gif87a and gif89 a. In one embodiment, it may be determined whether the picture is a picture in gif format by parsing the first 5 bytes of information of the picture binary stream. For example, if the first 5 bytes of information of the picture binary stream is "gif89a" (corresponding 16-ary information: 47 49 46 38 39 61) or "gif87a" (corresponding 16-ary information: 47 49 46 38 37 61), it is judged to be a picture in gif format. After determining that the picture is a gif-format moving picture, acquiring a target frame from the gif-format moving picture.
In some embodiments of the present disclosure, the moving image defaults to an image frame in a preset sequence of bits when not triggered to play. For example, some mobile terminals may not automatically play a default moving image when not triggered to play, but rather stop at an image frame of a preset sequence bit, such as a first image frame, an intermediate sequence image frame, or a last image frame of the moving image, for example, in order to save user traffic and reduce buffering. In these embodiments, the step S201 includes: and acquiring an image frame with a preset sequence of the dynamic image, and taking the image frame with the preset sequence as a target frame. That is, the target frame is a fixed one of a plurality of image frames of the moving image, and these embodiments are based on the recognition of the target type moving image by the target frame.
In other embodiments of the present disclosure, the embodiment method further comprises: returning to the target frame for acquiring the dynamic image in response to determining that the target frame does not contain the preset biological characteristics, and taking a subsequent image frame adjacent to the target frame as an updated target frame; or in response to determining that the target frame does not contain the preset biological characteristics, returning to the target frame for acquiring the dynamic image, and taking the previous image frame adjacent to the target frame as the updated target frame.
In these embodiments, the target frame is not a fixed frame, and is updated to be an image frame of a previous order bit or a subsequent order bit when the target frame does not contain the predetermined biometric feature. For example, the first image frame is a default target frame, and when the first image frame does not contain the preset biological characteristics, the target frame is automatically updated to the second image frame; when the second image frame does not contain the preset biological characteristics, automatically updating the target frame into a third image frame; and so on. The embodiment can prevent a user from deliberately hiding some information in a frame of the dynamic image which is not the default stop (the user is required to trigger the playing of the dynamic image to see), thereby further improving the accuracy of identifying the target type dynamic image.
In some embodiments of the present disclosure, it is determined that the dynamic image does not belong to the target type dynamic image in response to at least one of the following conditions being satisfied: determining that the target frame does not contain a preset biological feature; determining that the association degree is not greater than an association degree threshold value; and determining that the motion information does not accord with the preset condition.
In one embodiment, the target frame contains a preset biological feature, the association degree is larger than the association degree threshold value, and the motion information accords with a preset condition, so that the dynamic image can be determined to belong to the target type dynamic image, and under the identification result, the user can not upload the dynamic image to the network. If the above conditions cannot be satisfied at the same time, it is determined that the moving image does not belong to the target type moving image, and the user is allowed to upload the moving image to the network.
In some embodiments of the present disclosure, determining motion information of an organism associated with a preset biometric feature included in a plurality of image frames in step S203 includes: extracting optical flow information of at least two consecutive image frames including a target frame; and determining motion information corresponding to the optical flow information.
Optical flow is apparent motion of an image luminance pattern, which expresses changes in the image, and can be used to determine the motion of an object because it contains information about motion. At least two consecutive image frames may express a certain motion information. In this embodiment, the corresponding motion information can be quickly and accurately determined from the optical flow information of at least two consecutive image frames including the target frame. The at least two consecutive image frames including the target frame are, for example, the target frame and two image frames adjacent to each other before and after the target frame, or all image frames included in the moving image.
In some embodiments of the present disclosure, step S201 includes: in response to determining that the dynamic image does not belong to the abnormal dynamic image, a target frame of the dynamic image is acquired. The condition for determining the abnormal moving image is not limited, and for example, the abnormal moving image can be determined by at least one of: the method comprises the steps of failing to download the dynamic image, failing to identify the dynamic image format, incomplete display of at least one image frame of the dynamic image, and exceeding the aspect ratio of at least one image frame of the dynamic image by a set range. According to the embodiment, firstly, the abnormal dynamic image is screened out, and then the target type dynamic image is identified, so that the speed of identifying the target type dynamic image can be improved.
In some embodiments, the method of identifying a target type moving image further comprises: and in response to determining that the motion information meets preset conditions, performing treatment operation on the dynamic image and/or the account associated with the dynamic image, for example, deleting the dynamic image, sending out an uploading prohibition warning, or sealing the associated account. The operation of the step can be automatically operated by the network information auditing platform or manually operated by related personnel of the network information auditing platform.
As shown in fig. 3, taking a face as a preset biometric feature as an example, a method 300 for identifying a target type dynamic image according to an embodiment of the disclosure includes the following steps S301 to S311:
in step S301, a moving image uploaded by a user is acquired;
in step S302, it is determined whether the moving image belongs to an abnormal moving image (the determination condition of the abnormal moving image refers to the setting in the foregoing embodiment), if so, the flow is ended, otherwise, the flow goes to the following step S303;
in step S303, a first image frame of a moving image is acquired as a target frame;
In step S304, it is determined whether the target frame contains a face, if yes, the flow goes to step S305 below, otherwise, the flow is ended;
In step S305, determining a degree of association between the target frame and a preset type image model, where the preset type image model includes a face;
In step S306, it is determined whether the association degree is greater than the association degree threshold, if yes, the flow goes to step S307 below, otherwise, the flow is ended;
In step S307, optical flow information of all image frames of the moving image is extracted;
In step S308, motion information corresponding to the optical flow information is determined;
In step S309, it is determined whether the motion information shows the set periodicity, if yes, the flow goes to step S310 below, otherwise, the flow ends;
In step S310, it is determined that the moving image belongs to the target type moving image;
in step S311, the moving image is deleted, and the account associated with the moving image is recorded and blocked.
By adopting the technical scheme of the method, the accuracy of identifying the target type dynamic image can be effectively improved, and the limit requirement of the network content platform on the transmission network information can be further met.
As shown in fig. 4, an embodiment of the present disclosure further provides an apparatus 400 for identifying a target type moving image, including:
An acquisition unit 401 configured to acquire a target frame of a moving image, the target frame being one of a plurality of image frames included in the moving image;
a first determining unit 402 configured to determine a degree of association of the target frame with a preset type image model, the preset type image model including a preset biometric feature, in response to determining that the target frame includes the preset biometric feature;
a second determining unit 403 configured to determine motion information of an organism related to a preset biological feature included in the plurality of image frames in response to determining that the degree of association is greater than the degree of association threshold; and
The third determining unit 404 is configured to determine that the moving image belongs to the target type moving image in response to determining that the motion information meets a preset condition.
As described in the foregoing analysis, the device according to the foregoing embodiment of the disclosure may effectively improve accuracy of identifying the target type moving image, and may further meet the limitation requirement of the network content platform on the transmission network information.
In the technical scheme of the disclosure, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the user accord with the regulations of related laws and regulations, and the public order colloquial is not violated. Note that, the preset type image model and the target type moving image in this embodiment are not specific to a specific user, and cannot reflect personal information of a specific user.
As shown in fig. 5, a block diagram of an electronic device 500 of the present disclosure applied to a server or a client is a structural diagram, which is an example of a hardware device that may be applied to aspects of the present disclosure. Electronic device 500 is intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 5, the electronic device 500 includes a computing unit 501 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 502 or a computer program loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM503, various programs and data required for the operation of the electronic device 500 may also be stored. The computing unit 501, ROM 502, and RAM503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Various components in the device 500 are connected to the I/O interface 505, including: an input unit 506, an output unit 507, a storage unit 508, and a communication unit 509. The input unit 506 may be any type of device capable of inputting information to the electronic device 500, the input unit 506 may receive input numeric or character information and generate key signal inputs related to user settings and/or function control of the electronic device, and may include, but is not limited to, a mouse, a keyboard, a touch screen, a trackpad, a trackball, a joystick, a microphone, and/or a remote control. The output unit 507 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, video/audio output terminals, vibrators, and/or printers. Storage unit 508 may include, but is not limited to, magnetic disks, optical disks. The communication unit 509 allows the electronic device 500 to exchange information/data with other devices over a computer network such as the internet and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chipsets, such as bluetooth (TM) devices, 1302.11 devices, wiFi devices, wiMax devices, cellular communication devices, and/or the like.
The computing unit 501 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The calculation unit 501 performs the respective methods and processes described above, such as the aforementioned method of identifying a target type moving image. For example, in some embodiments, the method of identifying a target type dynamic image may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 500 via the ROM 502 and/or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the above-described method of identifying a target type moving image may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the above-described method of identifying a target type dynamic image by any other suitable means (e.g., by means of firmware).
The disclosed embodiments also provide a non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method steps of any of the embodiments described above.
According to another aspect of the present disclosure, there is also provided a computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements the method steps of any of the embodiments described above.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.
Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the foregoing methods, systems, and apparatus are merely exemplary embodiments or examples, and that the scope of the present invention is not limited by these embodiments or examples but only by the claims following the grant and their equivalents. Various elements of the embodiments or examples may be omitted or replaced with equivalent elements thereof. Furthermore, the steps may be performed in a different order than described in the present disclosure. Further, various elements of the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced by equivalent elements that appear after the disclosure.
Claims (12)
1. A method of identifying a target type moving image, comprising:
Acquiring a target frame of a dynamic image, wherein the dynamic image is a gif-format moving picture, and the target frame is one of a plurality of image frames contained in the dynamic image;
in response to determining that the target frame contains a preset biological feature, determining a degree of association of the target frame with a preset type image model, the preset type image model containing the preset biological feature;
Responsive to determining that the degree of association is greater than a degree of association threshold, determining motion information of an organism associated with the preset biometric feature contained in the plurality of image frames; and
And determining that the dynamic image belongs to the target type dynamic image in response to the fact that the motion information meets the preset condition, wherein the preset condition comprises periodicity of the motion information presentation setting.
2. The method of claim 1, wherein the moving image defaults to an image frame that is in a preset sequence when not triggered to play, and the acquiring the target frame of the moving image comprises:
And acquiring the image frame of the preset sequence bit of the dynamic image, and taking the image frame of the preset sequence bit as the target frame.
3. The method of claim 2, wherein the image frame of the preset sequence of bits is a first image frame, an intermediate sequence of image frames, or a last image frame of the dynamic image.
4. The method of claim 1, further comprising:
Returning the target frame of the acquired dynamic image and taking a subsequent image frame adjacent to the target frame as an updated target frame in response to determining that the target frame does not contain the preset biological feature; or alternatively
And returning the target frame of the acquired dynamic image in response to the fact that the target frame does not contain the preset biological characteristics, and taking the previous image frame adjacent to the target frame as an updated target frame.
5. The method of claim 1, wherein determining motion information of an organism associated with the preset biometric included in the plurality of image frames comprises:
Extracting optical flow information of at least two consecutive image frames containing the target frame; and
And determining motion information corresponding to the optical flow information.
6. The method of claim 1, wherein the acquiring the target frame of the moving image comprises:
in response to determining that the dynamic image does not belong to an abnormal dynamic image, acquiring a target frame of the dynamic image;
Wherein the determination condition of the abnormal dynamic image includes at least one of: the method comprises the steps of failing to download the dynamic image, failing to identify the dynamic image format, incomplete display of at least one image frame of the dynamic image, and exceeding the aspect ratio of at least one image frame of the dynamic image by a set range.
7. The method of claim 1, further comprising:
And responding to the fact that the motion information meets the preset condition, and performing treatment operation on the dynamic image and/or the account number associated with the dynamic image.
8. The method of claim 1, further comprising: determining that the dynamic image does not belong to the target type dynamic image in response to at least one of the following conditions being satisfied:
Determining that the target frame does not contain the preset biological feature;
Determining that the association is not greater than the association threshold; and
And determining that the motion information does not meet the preset condition.
9. An apparatus for recognizing a target type moving image, comprising:
An acquisition unit configured to acquire a target frame of a moving image, the moving image being a moving picture in gif format, the target frame being one of a plurality of image frames included in the moving image;
A first determining unit configured to determine, in response to determining that the target frame contains a preset biometric feature, a degree of association of the target frame with a preset type image model, the preset type image model containing the preset biometric feature;
a second determining unit configured to determine motion information of an organism related to the preset biometric feature contained in the plurality of image frames in response to determining that the degree of association is greater than a degree of association threshold; and
And a third determination unit configured to determine that the moving image belongs to a target type moving image in response to determining that the motion information meets a preset condition, wherein the preset condition includes a periodicity of the motion information presentation setting.
10. An electronic device, comprising:
A memory; and
A processor coupled to the memory, the processor configured to perform the method of any one of claims 1 to 8 based on instructions stored in the memory.
11. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the method according to any of claims 1 to 8.
12. A computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements the method according to any one of claims 1 to 8.
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