CN114390366A - Video processing method and device - Google Patents
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Abstract
The disclosure provides a video processing method and a video processing device, relates to the field of artificial intelligence, and particularly relates to a computer vision, image recognition and deep learning technical scene. The implementation scheme is as follows: acquiring description data of a video, the description data indicating a category corresponding to the video among a plurality of categories; obtaining a detection result indicating whether the video corresponds to the target classification based on the description data; determining the video as a video to be processed in response to the detection result indicating that the video corresponds to the target classification; and obtaining a video label of the video to be processed from a plurality of labels corresponding to the target classification.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence, and in particular, to computer vision, image recognition, and deep learning technology scenarios, and more particularly, to a video processing method, apparatus, electronic device, computer-readable storage medium, and computer program product.
Background
Artificial intelligence is the subject of research that makes computers simulate some human mental processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), both at the hardware level and at the software level. The artificial intelligence hardware technology generally comprises technologies such as a sensor, a special artificial intelligence chip, cloud computing, distributed storage, big data processing and the like, and the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, machine learning/deep learning, a big data processing technology, a knowledge graph technology and the like.
The video processing technology based on artificial intelligence enriches the forms and dimensions of the information contained in the video acquired by the user by understanding the video content, and improves the density of the information transmitted to the user. In the process of understanding the video content, image information in a video frame contained in the video is often identified to label the video, so that a user can search the video according to the label of the video, or a server can push the video for the user according to the label of the video.
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, unless otherwise indicated, the problems mentioned in this section should not be considered as having been acknowledged in any prior art.
Disclosure of Invention
The present disclosure provides a video processing method, apparatus, electronic device, computer-readable storage medium, and computer program product.
According to an aspect of the present disclosure, there is provided a video processing method including: obtaining description data of a video, the description data indicating a category corresponding to the video among a plurality of categories; obtaining a detection result indicating whether the video corresponds to a target classification based on the description data; determining the video as a video to be processed in response to the detection result indicating that the video corresponds to the target classification; and obtaining the video label of the video to be processed from a plurality of labels corresponding to the target classification.
According to another aspect of the present disclosure, there is provided a video processing apparatus acquisition unit configured to acquire description data of a video, the description data indicating a category corresponding to the video among a plurality of categories; a detection unit configured to obtain a detection result indicating whether the video corresponds to a target classification based on the description data; a determining unit configured to determine the video as a video to be processed in response to the detection result indicating that the video corresponds to the target classification; and a processing unit configured to obtain a video tag of the video to be processed from a plurality of tags corresponding to the object classification.
According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method according to embodiments of the present disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method according to the embodiments of the present disclosure.
According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program realizes the method according to embodiments of the present disclosure when executed by a processor.
According to one or more embodiments of the present disclosure, in the process of tagging videos, the data processing amount can be reduced, and the accuracy of tagging the videos can be improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the embodiments and, together with the description, serve to explain the exemplary implementations of the embodiments. The illustrated embodiments are for purposes of illustration only and do not limit the scope of the claims. Throughout the drawings, identical reference numbers 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, according to an embodiment of the present disclosure;
fig. 2 shows a flow diagram of a video processing method according to an embodiment of the present disclosure;
fig. 3 shows a flowchart of a process of acquiring a detection result indicating whether a video corresponds to a target classification in a video processing method according to an embodiment of the present disclosure;
fig. 4 shows a flowchart of a process of obtaining a video tag of a video to be processed from a plurality of tags corresponding to a target classification in a video processing method according to an embodiment of the present disclosure;
fig. 5 shows a flowchart of a process for acquiring a video tag of a video to be processed in a video processing method according to an embodiment of the present disclosure;
fig. 6 shows a flowchart of a process of extracting a plurality of video frames from a video to be processed in a video processing method according to an embodiment of the present disclosure may be implemented;
fig. 7 shows a block diagram of a video processing apparatus according to an embodiment of the present disclosure; and
FIG. 8 illustrates a block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those 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, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, the timing relationship, or the importance relationship of the elements, and such terms are used only 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, based on the context, they may also refer to different instances.
The terminology used in the description of the various described 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, if the number of elements is not specifically limited, the elements may be one or more. Furthermore, the term "and/or" as used in this disclosure is intended to encompass any and all possible combinations of the listed items.
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 embodiments 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 embodiments of the present disclosure, the server 120 may run one or more services or software applications that enable the video processing method to be performed.
In some embodiments, the server 120 may also provide other services or software applications that may include non-virtual environments and virtual environments. In certain 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, which may be executed by one or more processors. A user operating a client device 101, 102, 103, 104, 105, and/or 106 may, in turn, utilize one or more client applications to interact with the server 120 to take advantage of the services provided by these components. It should be understood 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.
A user may use client devices 101, 102, 103, 104, 105, and/or 106 to receive video processed according to the video processing methods of the present disclosure. 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 any number of client devices may be supported by the present disclosure.
Network 110 may be any type of network known to those skilled in the art that may support data communications using any of a variety of available protocols, including but not limited to TCP/IP, SNA, IPX, etc. By way of example only, 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 involving virtualization (e.g., one or more flexible pools of logical storage that may be virtualized to maintain virtual storage for the server). In various embodiments, the 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. The 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, and the like.
In some implementations, the server 120 may include one or more applications to analyze and consolidate data feeds and/or event updates received from users of the 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 embodiments, the server 120 may be a server of a distributed system, or a server incorporating a blockchain. The server 120 may also be a cloud server, or a smart cloud computing server or a smart cloud host with artificial intelligence technology. The cloud Server is a host product in a cloud computing service system, and is used for solving the defects of high management difficulty and weak service expansibility in the traditional physical host and Virtual Private Server (VPS) 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 the databases 130 may be used to store information such as audio files and video files. The data store 130 may reside in various 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 certain embodiments, the data store used by the server 120 may be a database, such as a relational database. One or more of these databases may store, update, and retrieve data to and from the database in response to the command.
In some embodiments, one or more of the 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 regular stores supported by a 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.
Referring to fig. 2, a video processing method 200 according to some embodiments of the present disclosure includes:
step S210: obtaining description data of a video, the description data indicating a category corresponding to the video among a plurality of categories;
step S220: obtaining a detection result indicating whether the video corresponds to a target classification based on the description data;
step S230: determining the video as a video to be processed in response to the detection result indicating that the video corresponds to the target classification;
step S240: and responding to the video corresponding to the target classification, and obtaining the video label of the video to be processed from a plurality of labels corresponding to the target classification.
The method comprises the steps of determining whether a video corresponds to a target classification or not based on description data corresponding to the video, determining the video as the video to be processed when the video corresponds to the target classification, determining video tags of the video to be processed from a plurality of tags corresponding to the target classification, screening the video to tag the video corresponding to the target classification, and enabling the data processing amount of the process of obtaining the video tags to be small due to the fact that the process of obtaining the video to be processed through screening the video is performed, and enabling the obtained video tags to be obtained from the tags corresponding to the screened target classification, so that the video tags of the video to be processed are accurate.
In the related art, video analysis is often performed on a single video, for example, in the process of analyzing information contained in the video, a matched tag is obtained by directly importing the video or a video frame sequence obtained through the video into a model, by extracting image information, and by matching the image information with various tags. When the description data volume is large, each video needs to be analyzed in the processing mode, so that the data processing volume is large, and the tag acquisition efficiency is low.
In the embodiment according to the disclosure, the video is screened firstly based on the description data of the video to obtain the video to be processed corresponding to the target classification, and then the video tags of the video to be processed are obtained from the plurality of tags corresponding to the target classification, so that the data processing amount is greatly reduced under the condition of large video amount.
For example, in a typical application according to the present disclosure, a server obtains a video from an upstream server, and tags the video so that the video contains more information. When the user acquires the video of the upstream server, the upstream server calls the tagged video from the server and provides the tagged video for the user. The upstream server side can screen a plurality of videos every day, for example, the videos can be tens of thousands of videos, and the method can be used for marking the videos needing to be marked, so that the data processing amount during marking the videos is greatly reduced, and the efficiency of marking the videos is improved.
In some embodiments, the description data is, for example, such that text data corresponding to the video is included, e.g., the description data includes several fields, such as "vid", "video _ url", "cat", "sub _ cat", and the like. Wherein the "category" field indicates the first level category of the video, the "sub _ category" field indicates the second level category of the video, and each "category" field contains a corresponding "sub _ category" field.
In some embodiments, the "cat" field, for example, includes: hedonic, natural, recreational, gaming, movie & TV, music, constellation sports, dance, culture, sports, digital, hand work, current affairs, fashion, life, photography, society, agriculture, forestry, car, bat man, mother and infant, delicacy, travel, history, science and technology, military affairs, education, vehicle, health preserving, home furnishing, international, fun, cartoon, pet, finance and so on.
In some embodiments, the "sub _ cat" field corresponding to the "cat" field being a synthesis includes, for example: comprehensive hedging, comprehensive employment of workplace, comprehensive employment of music, show, figurine, vocals, mutual and relative hedging, comprehensive dance, cultural hedging, talk show, challenge hedging, fashion hedging, emotional hedging, parent and child hedging, magic, star show, tourist hedging, treasure appreciation, and the like.
In some embodiments, it should be understood that the embodiments of the present disclosure and the example that the video description data includes the "cate" field indicating the first-level category of the video and the "sub _ cate" field indicating the second-level category of the video are only exemplary, and the video description data may also include only the field indicating the first-level category or indicate multiple levels (e.g., a third-level category subdivided in the second-level category, a fourth-level category subdivided in the third-level category, etc.), and are not limited herein.
Meanwhile, it is to be understood that the category of the video is not the same as the target classification. The category of the video is defined in the description data of the video, which is sourced from the service end of the video source end; the target classification is a classification for labeling the video according to the requirement set by the requirement, and is determined by a server at the video processing end.
In some embodiments, the description data includes a first field indicating a first level category corresponding to the video in a plurality of first level categories, each of the plurality of first level categories corresponding to a plurality of second level categories, the description data further including a second field indicating a second level category corresponding to the video in a respective plurality of second level categories. As shown in fig. 3, obtaining a detection result indicating whether the video corresponds to a target classification includes:
step S310: combining the first and second fields; and
step S320: obtaining the detection result based on the combined field and the target classification.
The data processing amount is further reduced by combining fields in the description data and obtaining a detection result based on the combined fields and the target classification.
In some embodiments, for the target classification, a corresponding filtering rule may be set to filter the video. In some embodiments, a plurality of screening fields corresponding to the target category are set, and a video corresponding to the combined field is determined to correspond to the target category in response to the combined field corresponding to one of the plurality of screening fields by traversing the combined field of the video through the plurality of screening fields of the target category. For example, the target is classified as "plant", and setting the screening field includes: "nature _ plant", "home _ horticulture". After a video indicating that the "show" field of the first-level category is "home" and the "sub _ show" field of the second-level category is "gardening" is obtained, since the combined field of the "show" field and the "sub _ show" field of the video is "home _ gardening", it may be determined that the video corresponds to the target category "vegetation".
In some embodiments, the detection result is obtained by obtaining a similarity of the combined field and the target classification.
In some embodiments, after determining that the video corresponds to the target category, a video connection is obtained based on a "video _ url" field in the description data of the video, and image data of the video is obtained based on the link (e.g., by downloading).
In the process of determining whether the video corresponds to the target classification, only the description data of the processed video is indicated, and the image data of the video is not involved.
In the following, a process of further obtaining a video tag of a video to be processed after the video is determined as the video to be processed according to an embodiment of the present disclosure is further described.
In some embodiments, as shown in fig. 4, obtaining the video tag of the video to be processed from the plurality of tags corresponding to the object classification includes:
step S410: for each video frame in a plurality of video frames of the video to be processed, acquiring a video frame label of the video frame and the probability corresponding to the video frame label from the plurality of labels; and
step S420: and acquiring the video label of the video to be processed based on the video frame label of each video frame in the plurality of video frames and the probability corresponding to the video frame.
The video label of the video to be processed is obtained by obtaining the video frame label of each video frame in a plurality of video frames of the video to be processed and the probability corresponding to the video frame label, so that the obtained video label of the video to be processed is further accurate.
In some embodiments, a video frame tag of the video frame and a probability corresponding to the video frame tag are obtained from the plurality of tags by an object tag model corresponding to an object classification
In some embodiments, the target label model corresponding to the target classification is obtained by pre-training the neural network model.
In some embodiments, the neural network model employs a Res3Net neural network framework. The Res3Net neural network framework obtains a prediction result corresponding to the input image by performing multi-scale processing on the input image, and further reduces data processing amount in the multi-scale processing process, so that the process of labeling videos is less in data processing amount and high in efficiency, and meanwhile, the accuracy of the labeled labels is ensured.
The Bottleneck block module included in the Res3Net neural network framework according to some embodiments of the present disclosure further reduces the amount of computation in the computation process by performing dimension reduction and then dimension increase on the input features.
In some embodiments, the target tag model obtains a probability corresponding to each tag in the plurality of tags based on the input video frame, and takes the tag with the highest probability corresponding to the plurality of tags as the video frame tag of the video frame of the input target tag model.
In some embodiments, obtaining the video tag of the processed video based on the video frame tag of each of the plurality of video frames and the probability that the video frame corresponds to the video frame comprises: and taking the video frame label with the corresponding probability larger than the threshold value as the video frame label of the video to be processed.
In some embodiments, as shown in fig. 5, obtaining the video tag of the video to be processed includes:
step S510: for each video frame in the plurality of video frames, in response to the fact that the probability corresponding to the video frame tag of the video frame is larger than a preset probability threshold value, determining the video frame tag of the video frame as a candidate tag to obtain a candidate tag set of the video to be processed; and
step S520: for each candidate tag in the candidate tag set, in response to determining that the number of video frames corresponding to the candidate tag in the plurality of video frames is not less than a preset number threshold, determining the candidate tag as a video tag of the video to be processed.
In the process of obtaining the video tags of the video to be processed, the video frame tags corresponding to each of the plurality of video frames are screened, and the video frame tags of which the corresponding probability is greater than a preset probability threshold and the video frame tags of which the number of the corresponding video frames in the plurality of video frames is greater than a preset number threshold are used as the video tags of the video to be processed, so that the accuracy of the video tags is further improved.
In some embodiments, different preset probability thresholds and preset number thresholds may be set according to the accuracy requirements of the video tags. In one example, the predetermined probability threshold is 0.94 and the predetermined number threshold is 3.
In some embodiments, only the category of the video to be processed is obtained in step S210, and a plurality of video frames are further extracted from the video to be processed, so as to obtain the video frame tag and the probability corresponding to the video frame tag of each of the plurality of video frames in step S220.
In some embodiments, a "video _ url" field in the description data is extracted to obtain a downloadable video link, and a number of video frames are extracted from the video to be processed after the video is downloaded.
In some embodiments, the number of the plurality of video frames is not greater than the preset threshold.
The number of the plurality of video frames is set to be not more than a preset threshold value, so that excessive computing resources are prevented from being consumed due to excessive number of processed video frames in the processing process.
In some embodiments, the threshold setting 150 is preset.
In some embodiments, the length of the video to be processed is relatively short, for example, several tens of seconds to several minutes, and a plurality of video frames of the video to be processed are obtained by extracting one frame of video frame for each preset time period. In one example, one video frame is extracted every 2 s.
In some embodiments, the length of the video to be processed is relatively long, for example, one hour, by obtaining a video segment from the video to be processed and extracting the plurality of video frames from the video segment.
In some embodiments, as shown in fig. 6, extracting the plurality of video frames from the video to be processed includes:
step S610: responding to the fact that the time length of the video to be processed is larger than a preset time length, and acquiring a subsection of the video to be processed based on the starting time point of the video to be processed, wherein the time length of the subsection is the preset time length; and
step S620: extracting the plurality of video frames from the segment based on a preset time interval.
The inventors have found that the content of the beginning segment of the video often represents the content of the video as a whole, and that the segment based on the beginning of the video is sufficient to obtain a video tag that represents the content of the video. Therefore, in the embodiment according to the present disclosure, by processing a plurality of video frames in the beginning segment of the video to be processed to obtain the video tag of the video, it is possible to avoid processing an excessive amount of data and to excessively consume system resources.
In some embodiments, the predetermined time period is 5min and the predetermined time interval is 2 s.
In some embodiments, after obtaining the video tag of the video to be processed, the video tag of the video to be processed is associated with a corresponding video frame in the plurality of video frames, so that when the video to be processed is played, the video tag of the video to be processed is shown on the corresponding video frame.
The video tags of the videos to be processed are associated with the corresponding video frames in the videos to be processed, so that a user can display the video tags corresponding to the videos when the videos are played, the information density of the videos is improved, the cognitive and information expansion requirements of the user are met, and the user experience is improved.
In some embodiments, new description data is generated by processing the description data of the video to be processed and returned to the server, so that the server invokes the new description data when providing the video corresponding to the user. In some embodiments, the new description data includes a video tag and a time at which a video frame corresponding to the video tag occurred.
According to an embodiment of the present disclosure, there is also provided a video processing apparatus, as shown in fig. 7, the apparatus 700 includes: an acquisition unit 710 configured to acquire description data of a video, the description data indicating a category detection unit 720 corresponding to the video among a plurality of categories, configured to acquire a detection result indicating whether the video corresponds to a target category based on the description data; a determining unit 730 configured to determine the video as a to-be-processed video in response to the detection result indicating that the video corresponds to the target classification; and a processing unit 740 configured to obtain a video tag of the video to be processed from a plurality of tags corresponding to the object classification.
In some embodiments, the description data includes a first field indicating a first level category corresponding to the video in a plurality of first level categories, each of the plurality of first level categories corresponding to a plurality of second level categories, the description data further includes a second field indicating a second level category corresponding to the video in a respective plurality of second level categories, the detection unit 720 includes: a combining unit configured to combine the first field and the second field to obtain a combined field; and a detection subunit configured to obtain the detection result based on the combined field and the target classification.
In some embodiments, the processing unit 740 comprises: the calculation unit is configured to acquire, for each of a plurality of video frames of the video to be processed, a video frame tag of the video frame and a probability corresponding to the video frame tag from the plurality of tags; and the video label acquisition unit is configured to acquire the video label of the video to be processed based on the video frame label of each video frame in the plurality of video frames and the probability corresponding to the video frame.
In some embodiments, the video tag obtaining unit includes: a first determining unit, configured to, for each of the plurality of video frames, in response to determining that the probability corresponding to the video frame tag of the video frame is greater than a preset probability threshold, determine the video frame tag of the video frame as a candidate tag to obtain a candidate tag set of the video to be processed; and a second determining unit configured to determine, for each candidate tag in the candidate tag set, in response to determining that the number of video frames corresponding to the candidate tag in the plurality of video frames is not less than a preset number threshold, the candidate tag as a video tag of the video to be processed.
In some embodiments, the processing unit 740 comprises: a video frame obtaining unit configured to extract the plurality of video frames from the video to be processed, wherein the number of the plurality of video frames is not greater than a preset threshold.
In some embodiments, the video frame acquisition unit comprises: a segment obtaining unit, configured to obtain a segment of the video to be processed based on a starting time point of the video to be processed in response to a time length of the video to be processed being greater than a preset time length, where the time length of the segment is the preset time length; and an extraction unit configured to extract the plurality of video frames from the segment based on a preset time interval.
According to an embodiment of the present disclosure, there is also provided an electronic apparatus including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method according to embodiments of the present disclosure.
There is also provided, in accordance with an embodiment of the present disclosure, a non-transitory computer-readable storage medium having stored thereon computer instructions for causing the computer to perform the method according to an embodiment of the present disclosure.
There is also provided, in accordance with an embodiment of the present disclosure, a computer program product, comprising a computer program, wherein the computer program, when executed by a processor, implements the method in accordance with an embodiment of the present disclosure.
In the technical scheme of the disclosure, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the personal information of the related user are all in accordance with the regulations of related laws and regulations and do not violate the good customs of the public order.
Referring to fig. 8, a block diagram of a structure of an electronic device 800, which may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic device 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 phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 8, the apparatus 800 includes a computing unit 801 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)802 or a computer program loaded from a storage unit 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The calculation unit 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
A number of components in the device 800 are connected to the I/O interface 805, including: an input unit 806, an output unit 807, a storage unit 808, and a communication unit 809. The input unit 806 may be any type of device capable of inputting information to the device 800, and the input unit 806 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device, and may include, but is not limited to, a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and/or a remote control. Output unit 807 can be any type of device capable of presenting information and can include, but is not limited to, a display, speakers, a video/audio output terminal, a vibrator, and/or a printer. The storage unit 808 may include, but is not limited to, a magnetic disk, an optical disk. The communication unit 809 allows the device 800 to exchange information/data with other devices via 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, 802.11 devices, WiFi devices, WiMax devices, cellular communication devices, and/or the like.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a 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 that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes 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 codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. 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. A 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 a pointing device (e.g., a mouse or a 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 can 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, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end 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 back-end, 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 clients and servers. A client and server are generally 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 with a combined blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be performed in parallel, sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the above-described 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 as issued and their equivalents. Various elements in the embodiments or examples may be omitted or may be replaced with equivalents thereof. Further, the steps may be performed in an order different from that described in the present disclosure. Further, various elements in 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 with equivalent elements that appear after the present disclosure.
Claims (15)
1. A video processing method, comprising:
obtaining description data of a video, the description data indicating a category corresponding to the video among a plurality of categories;
obtaining a detection result indicating whether the video corresponds to a target classification based on the description data;
determining the video as a video to be processed in response to the detection result indicating that the video corresponds to the target classification; and
and obtaining the video label of the video to be processed from a plurality of labels corresponding to the target classification.
2. The method of claim 1, wherein the description data includes a first field indicating a first level category corresponding to the video in a plurality of first level categories, each of the plurality of first level categories corresponding to a plurality of second level categories, the description data further including a second field indicating a second level category corresponding to the video in a respective plurality of second level categories, the obtaining a detection result indicating whether the video corresponds to a target classification including:
combining the first field and the second field to obtain a combined field; and
obtaining the detection result based on the combined fields and the target classification.
3. The method of claim 1, wherein the obtaining a video tag of the video to be processed from a plurality of tags corresponding to the target classification comprises:
for each video frame in a plurality of video frames of the video to be processed, acquiring a video frame label of the video frame and the probability corresponding to the video frame label from the plurality of labels; and
and acquiring the video label of the video to be processed based on the video frame label of each video frame in the plurality of video frames and the probability corresponding to the video frame.
4. The method of claim 3, wherein the obtaining the video tag of the video to be processed comprises:
for each video frame in the plurality of video frames, in response to the fact that the probability corresponding to the video frame tag of the video frame is larger than a preset probability threshold value, determining the video frame tag of the video frame as a candidate tag to obtain a candidate tag set of the video to be processed; and
for each candidate tag in the candidate tag set, in response to determining that the number of video frames corresponding to the candidate tag in the plurality of video frames is not less than a preset number threshold, determining the candidate tag as a video tag of the video to be processed.
5. The method of claim 3 or 4, wherein said obtaining a video tag of the video to be processed from a plurality of tags corresponding to the object classification further comprises:
extracting the plurality of video frames from the video to be processed, wherein the number of the plurality of video frames is not more than a preset threshold value.
6. The method of claim 5, wherein extracting the plurality of video frames from the video to be processed comprises:
responding to the fact that the time length of the video to be processed is larger than a preset time length, and acquiring a subsection of the video to be processed based on the starting time point of the video to be processed, wherein the time length of the subsection is the preset time length; and
extracting the plurality of video frames from the segment based on a preset time interval.
7. A video processing apparatus comprising:
an acquisition unit configured to acquire description data of a video, the description data indicating a category corresponding to the video among a plurality of categories;
a detection unit configured to obtain a detection result indicating whether the video corresponds to a target classification based on the description data;
a determining unit configured to determine the video as a video to be processed in response to the detection result indicating that the video corresponds to the target classification; and
a processing unit configured to obtain a video tag of the video to be processed from a plurality of tags corresponding to the object classification.
8. The apparatus of claim 7, wherein the description data comprises a first field indicating a first level category corresponding to the video in a plurality of first level categories, each of the plurality of first level categories corresponding to a plurality of second level categories, the description data further comprising a second field indicating a second level category corresponding to the video in a respective plurality of second level categories, the detection unit comprising:
a combining unit configured to combine the first field and the second field to obtain a combined field; and
a detection subunit configured to obtain the detection result based on the combined field and the target classification.
9. The apparatus of claim 7, wherein the processing unit comprises:
the calculation unit is configured to acquire, for each of a plurality of video frames of the video to be processed, a video frame tag of the video frame and a probability corresponding to the video frame tag from the plurality of tags; and
a video tag obtaining unit, configured to obtain a video tag of the video to be processed based on a video frame tag of each of the plurality of video frames and a probability corresponding to the video frame.
10. The apparatus of claim 9, wherein the video tag obtaining unit comprises:
a first determining unit, configured to, for each of the plurality of video frames, in response to determining that the probability corresponding to the video frame tag of the video frame is greater than a preset probability threshold, determine the video frame tag of the video frame as a candidate tag to obtain a candidate tag set of the video to be processed; and
a second determining unit configured to determine, for each candidate tag in the candidate tag set, in response to determining that the number of video frames corresponding to the candidate tag in the plurality of video frames is not less than a preset number threshold, the candidate tag as a video tag of the video to be processed.
11. The apparatus of claim 9 or 10, wherein the processing unit further comprises:
a video frame obtaining unit configured to extract the plurality of video frames from the video to be processed, wherein the number of the plurality of video frames is not greater than a preset threshold.
12. The apparatus of claim 10, wherein the video frame acquisition unit comprises:
a segment obtaining unit, configured to obtain a segment of the video to be processed based on a starting time point of the video to be processed in response to a time length of the video to be processed being greater than a preset time length, where the time length of the segment is the preset time length; and
an extraction unit configured to extract the plurality of video frames from the segment based on a preset time interval.
13. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.
14. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-6.
15. A computer program product comprising a computer program, wherein the computer program realizes the method of any one of claims 1-6 when executed by a processor.
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