WO2020119508A1 - 视频切割方法、装置、计算机设备和存储介质 - Google Patents
视频切割方法、装置、计算机设备和存储介质 Download PDFInfo
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Definitions
- the present application relates to a video cutting method, device, computer equipment and storage medium.
- a video cutting method, device, computer device, and storage medium are provided.
- a video cutting method includes:
- the result of the recognition of the speech and the preset rules for the trigger of the operation the result of the recognition of the operation is obtained;
- the video stream data is cut according to the cut point identification to obtain video segment data.
- a video cutting device includes:
- the identification data extraction module is used to extract the video data to be identified from the video stream data, and extract the image data and audio data from the video data to be identified;
- the dot recognition processing module is used to input the image data into the preset dot behavior recognition model to obtain the dot behavior recognition result, and input the audio data into the preset dot behavior recognition model to obtain the dot voice recognition result;
- the dot-result acquisition module is used to obtain the dot-recognition result according to the dot-response recognition result, the dot-speech voice recognition result, and the preset dot-striking trigger rules;
- the cutting mark adding module is used to add a cutting point mark to the video data to be recognized when the type of the dot recognition result is operation dotting;
- the video cutting module is used to cut the video stream data according to the cutting point identification to obtain video segment data.
- a computer device includes a memory and one or more processors.
- the memory stores computer-readable instructions.
- the one or more processors are executed The following steps:
- the result of the recognition of the speech and the preset rules for the trigger of the operation the result of the recognition of the operation is obtained;
- the video stream data is cut according to the cut point identification to obtain video segment data.
- One or more non-volatile computer-readable storage media storing computer-readable instructions.
- the computer-readable instructions When executed by one or more processors, the one or more processors perform the following steps:
- the result of the recognition of the speech and the preset rules for the trigger of the operation the result of the recognition of the operation is obtained;
- the video stream data is cut according to the cut point identification to obtain video segment data.
- FIG. 1 is an application scene diagram of a video cutting method according to one or more embodiments.
- FIG. 2 is a schematic flowchart of a video cutting method according to one or more embodiments.
- FIG. 3 is a schematic flow chart of responding to a dot cutting command according to one or more embodiments.
- FIG. 4 is a schematic flowchart of a video cutting method in another embodiment.
- FIG. 5 is a structural block diagram of a video cutting device according to one or more embodiments.
- FIG. 6 is an internal structure diagram of a computer device according to one or more embodiments.
- the video cutting method provided by this application can be applied in the application environment shown in FIG. 1.
- the recording device 102 communicates with the server 104 through the network through the network.
- the recording device 102 performs video recording and sends the recorded video stream data to the server 104.
- the server 104 extracts image data and audio data from the video data to be recognized obtained from the video stream data, and inputs the image data and audio data respectively to the corresponding
- the management recognition result is obtained according to the obtained management behavior recognition result, management speech recognition result and preset management rule.
- the management recognition type is operation management, Add a cut point mark to the video data to be recognized, and finally cut the video stream data according to the cut point mark to obtain video segment data.
- the recording device 102 may be, but not limited to, various video recording cameras, or terminals with video recording functions, such as personal computers, notebook computers, smart phones, tablets, and portable wearable devices.
- the server 104 may use an independent server or It is realized by a server cluster composed of multiple servers.
- a video cutting method is provided. Taking the method applied to the server 104 in FIG. 1 as an example for illustration, it includes the following steps:
- Step S201 Extract video data to be identified from video stream data, and extract image data and audio data from the video data to be identified.
- the video data to be recognized is extracted from the video stream data.
- the video stream data is video data that needs to be cut, and can be recorded by a recording device.
- the video stream data may be the video data captured by the camera in real time during the dual recording process.
- the to-be-recognized video data is video data of a preset recognition length.
- the recognition length is set according to actual requirements, and the corresponding cutting point identifier can be added by performing dot recognition on the video data to be recognized. Dot recognition by pre-recognizing the video data to be recognized can realize real-time cutting of the recorded video data to ensure the timeliness of the video cutting and improve the efficiency of video cutting.
- video data is composed of two parts: video and audio, and both video and audio parts can be identified.
- image data and audio data are extracted from the video data to be recognized, so that the image data and the audio data in the video data to be recognized are separately recognized and processed, so that the video image can be recognized Whether or not there is a dot behavior in the video, or whether there is a dot voice in the video and audio, which realizes the dot behavior recognition of the image behavior and audio voice, and improves the accuracy of the dot recognition.
- Step S203 Input the image data into the preset dot behavior recognition model to obtain the dot behavior recognition result, and input the audio data into the preset dot behavior recognition model to obtain the dot voice recognition result.
- the image data and audio data are input into the corresponding dot behavior recognition model and dot voice recognition model for dot recognition, respectively.
- the hitting behavior recognition model may be based on an artificial neural network algorithm, which is obtained by training the historical hitting behavior data of the business personnel of the business system in the corresponding business scenario, for example, it may be a hitting action such as an applause action, a hand raising action, a tapping action, etc. ;
- the dotted voice recognition model can be obtained by training the historical dotted voice data of the business personnel, for example, the keyword voice can be dotted, such as "first, second, third" and other keywords.
- image data is input into a preset dot behavior recognition model to perform dot behavior recognition, and a dot behavior recognition result is obtained;
- audio data is input into a preset dot behavior recognition model to perform dot voice recognition To get the results of dotted voice recognition.
- Step S205 Based on the dotted behavior recognition result, the dotted voice recognition result, and the preset dotted trigger rule, the dotted recognition result is obtained.
- a preset triggering rule for inquiring is queried, which is set according to actual business requirements. If it can be set to take the result of the recognition of the beating behavior and the result of the recognition of the beating voice, that is, as long as the type of any one of the beating behavior recognition result and the beating voice recognition result is the operation beating, that is, when the cutting point mark needs to be added, the beating will be triggered to obtain
- the results of RBI recognition are operations RBI; can also be taken and processed for RBI behavior recognition results and RBI voice recognition results, that is, only when the types of RBI behavior recognition results and RBI voice recognition results are both RBI operations, the RBI is triggered and the resulting RBI
- the type of recognition result is operation management.
- Step S207 When the type of the dot recognition result is the operation dot, add a cut point identifier to the video data to be recognized.
- the type of the dot recognition result is judged.
- the type of the dot recognition result is the operation dot, it indicates that the image data and/or audio data in the video data to be recognized has triggered the dot.
- the video data to be recognized is For the video cutting position, perform dot processing on it, and specifically, a cutting point identifier can be added to the video data to be recognized. Among them, the cutting point identifier is used to identify the cutting point of the video cutting. When cutting the video stream data, the cutting point identifier can be directly searched for cutting processing.
- the cutting point identifier may be a cutting label.
- the key frame is determined from the video data to be recognized according to the preset label addition rule, such as the video frame to be recognized
- the first frame of is used as a key frame, and a cutting label is added to the key frame.
- the cutting label may include, but is not limited to, a cutting point number, a cutting time value, and the like.
- Step S209 Cut the video stream data according to the cut point identifier to obtain video segment data.
- image data and audio data are extracted from the video data to be recognized obtained from the video stream data, and the image data and audio data are respectively input into the corresponding preset dot recognition model and dot speech recognition model, and then According to the obtained dot-action recognition result, dot-voice recognition result, and preset dot-trigger trigger rules, the dot-recognition result is obtained.
- the type of dot-recognition result is operation dot
- the cut-point mark is added to the video data to be recognized, and finally the cut-point mark Cut the video stream data to get the video segment data.
- the dot data can be identified according to the image data and audio data in the to-be-recognized video data, and the cut point identifier can be added, and no manual dot operation is required, which improves the processing efficiency of the video cut.
- extracting the video data to be identified from the video stream data includes: acquiring the video stream data; determining the video stream identification length; and extracting the video data to be identified from the video stream data according to the video stream identification length.
- the video stream data directly recorded by the recording device 102 it is not possible to directly perform dot recognition processing, and it needs to be split into video data to be recognized with a fixed recognition length, and the dot recognition is performed by the video data to be recognized.
- the video stream data is first obtained, specifically, the real-time recorded video stream data may be directly received from the recording device 102, or may be stored from a preset memory Read the video stream data after recording.
- the video stream recognition length is determined, and the video stream recognition length is set according to actual needs, for example, it can be set according to the input requirements of the dot behavior recognition model and the dot voice recognition model, or can be set according to the processing resources of the server 104 set.
- the video data to be recognized that meets the recognition length of the video stream can be sequentially extracted from the video stream data, and then the extracted video data to be recognized can be subjected to subsequent dot recognition processing.
- the image data is input into a preset dot-behavior recognition model to obtain a dot-behavior recognition result
- the audio data is input into a preset dot-behavior recognition model
- the result of the dot-behavior recognition includes: determining to be recognized
- the video data corresponds to the identification information of the business personnel to which it belongs; querying the identification information corresponds to the preset dot behavior recognition model and dot speech recognition model; extracting image feature data from the image data, and extracting audio feature data from the audio data; and will
- the image feature data is input into the dot behavior recognition model to obtain the dot behavior recognition result
- the audio feature data is input into the dot speech recognition model to obtain the dot speech recognition result.
- the RBI behavior recognition model and the RBI speech recognition model are both trained based on the historical RBI data of each business person in the business system.
- different business systems will have different management requirements for operations, and different business personnel will also have different management habits for operations.
- each business service window is provided with a recording device 102, which can determine the corresponding business personnel according to the source of the video data to be identified, that is, according to the recording device 102, and further query the corresponding identification information of the business personnel.
- the identification information may be, but not limited to, employee ID, employee name, etc. that can uniquely identify business personnel.
- the RBI behavior recognition model and the RBI speech recognition model are based on the historical RBI behavior data and historical RBI voice data of the corresponding business personnel, respectively After training, the identification of the dots is highly targeted and the recognition accuracy is high.
- image feature data is extracted from the image data, and the image feature data is input into the RBI behavior recognition model to obtain the RBI behavior recognition result.
- the audio feature data is extracted from the audio data, and the audio feature data is input into the dotted voice recognition model to obtain the dotted voice recognition result.
- the method before querying the identification information corresponding to the preset dot-action recognition model and dot-speech recognition model respectively, the method further includes: acquiring historical behavior image data and historical dot-sound voice data from the business system; Image data and historical RBI voice data are classified according to business personnel to obtain historical behavior image data corresponding to each business personnel and historical RBI voice data corresponding to each business personnel; training historical image data corresponding to each business personnel to obtain RBI behavior recognition model ; And train the historical dotted voice data corresponding to each business staff to get the dotted voice recognition model.
- the historical behavior image data can be the dot image data captured by each business personnel in the business system during the process of business review, for example, it can include applause, raising hands, hands crossed, nodding, etc.; historical dot voice data Similar to historical behavioral image data, such as keyword sentences, "Question X", "OK, thank you", etc.
- each business person will have different personal habits, and the corresponding historical behavior image data and historical dotted voice data will perform differently. Therefore, according to the business personnel, the historical behavior image data and historical dotted voice data will be different. Classify and build the corresponding RBI behavior recognition model and RBI speech recognition model for each business personnel.
- the historical behavior image data corresponding to each business staff is trained to obtain a dotted behavior recognition model; the historical dotted speech data corresponding to each business staff is trained to obtain a dotted speech recognition model.
- the historical behavior image data can be divided into a training sample set and a test sample set, and the training sample set is trained through a supervised learning method to obtain a test behavior model to be tested, and then the test sample behavior model is identified through the test sample set Accuracy test, after the recognition accuracy test is passed, get the RBI behavior recognition model.
- the training process of the dotted speech recognition model is similar to the dotted behavior recognition model.
- the voice recognition results of the dot and the preset trigger rules for the dot include: querying the preset trigger rules for the dot, the trigger rules for the trigger include the behavior trigger rules and the voice trigger rules ; Compare the dotted behavior recognition result with the behavior trigger rule to get the behavior trigger result; compare the dotted speech recognition result with the voice trigger rule to get the voice trigger result; and according to the behavior trigger result and the voice trigger result, get the dotted recognition result.
- the results of the dot-recognition are obtained.
- query preset preset trigger rules are set according to actual business needs, and can be set according to the type of business and the habits of business personnel. For example, when the business data is identified in the image data When applauding, or when the key sentence of "X question" is recognized in the audio data, it is considered to trigger the RBI.
- the dot triggering rules include behavior triggering rules and voice triggering rules, which respectively correspond to dot data recognition of image data and voice data.
- the behavior triggering result and the voice triggering result are combined to obtain the hitting recognition result.
- the behavior triggering result and the voice triggering result can be ORed, that is, when either type of the behavior triggering result and the voice triggering result is the operation hitting, the hitting point is obtained
- the type of the recognition result is operation dotting, and the cutting point mark is added to the video data to be recognized.
- step of responding to the dot-cutting instruction specifically including:
- Step S301 When the dot-cutting instruction is received, the cutting time value of the dot-cutting instruction is determined.
- the dot-cutting command can be sent from outside, for example, the business personnel click the relevant dot-cutting button; the cutting time value is the sending time of the dot-cutting command, reflecting the time axis position in the video stream data where the dot-setting operation needs to be performed.
- Step S303 Determine the cutting video frame corresponding to the cutting time value in the video data to be identified.
- the cutting video frame corresponding to the cutting time value is determined from the video data to be recognized.
- the dot cutting command is sent externally, it indicates that the video frame corresponding to the moment in the video data to be recognized needs to be manipulated and the corresponding cut video can be determined from the time axis of the video data to be recognized according to the cutting moment value of the dot cutting instruction frame.
- Step S305 Add a cutting point identifier to the cutting video frame.
- a cut point identifier is added to the cut video frame.
- the cut point identifier is used to identify the cut point of the video cut.
- the cut point identifier can be directly searched for cutting processing.
- Step S307 Return to cutting the video stream data according to the cutting point identifier to obtain video segment data.
- the dot cutting instruction sent from the outside is received in real time, and the video cutting processing is performed according to the dot cutting instruction to achieve external control of the video cutting. It can effectively expand the operation diversity of video cutting and improve the efficiency of video cutting processing.
- the method further includes: extracting audio segment data from the video segment data; querying a preset voice recognition model; inputting the audio segment data into the voice recognition model to obtain a translation of the video segment data Data; and determine the service type corresponding to the video segment data according to the translation data, and store the video segment data to a storage location corresponding to the service type.
- the video segment data obtained by cutting the video stream data after the video segment data obtained by cutting the video stream data is obtained, it can be stored in a corresponding storage location according to the service type of each video segment data.
- audio segment data is extracted from the video segment data, and the audio segment data includes dialog data in the video segment data, and the service type corresponding to the video segment data can be determined according to the audio segment data.
- the voice recognition model can perform voice recognition on the input voice data to obtain corresponding translation data.
- the audio segment data is input into the speech recognition model to obtain translation data of the video segment data.
- the translation data may be data in text form, and the service type corresponding to the video segment data may be determined according to the translation data.
- business keywords can be extracted from the translation data, and the corresponding business type can be matched according to the obtained business keywords.
- After determining the service type corresponding to the video segment data store the video segment data in the storage location corresponding to the service type. For example, the preset storage location corresponding to the service type can be queried, and the video segment data is stored in the storage location, thereby achieving automatic classification storage of the video segment data.
- a video cutting method including:
- Step S401 Obtain video stream data
- Step S402 Determine the identification length of the video stream
- Step S403 Extract the video data to be identified from the video stream data according to the video stream identification length
- Step S404 Extract image data and audio data from the video data to be recognized.
- the server 104 receives the video stream data sent by the recording device 102, and determines the length of the video stream identification set according to actual needs, and in accordance with the video stream identification length, sequentially extracts from the video stream data to meet the video stream identification The length of the to-be-recognized video data, and then the extracted to-be-recognized video data is subjected to subsequent dot recognition processing.
- Step S405 Determine the identity information of the business personnel to which the video data to be identified corresponds
- Step S406 Querying the identity identification information respectively corresponding to the pre-set behavior recognition model and the speech recognition model;
- Step S407 extract image feature data from the image data, and extract audio feature data from the audio data
- Step S408 Input the image feature data into the dot behavior recognition model to obtain a dot behavior recognition result, and input the audio feature data into the dot speech recognition model to obtain a dot speech recognition result.
- the source of the video data to be identified is determined according to the recording device 102, and the corresponding business personnel are determined, and further identification information corresponding to the business personnel is queried.
- the identity information is the employee number and/or employee Name.
- the RBI behavior recognition model and RBI speech recognition model are trained based on the historical RBI behavior data and historical RBI speech data of the corresponding business personnel, respectively. It has strong pertinence and high recognition accuracy.
- the image feature data is extracted from the image data, and the image feature data is input into the dot behavior recognition model to obtain the dot behavior recognition result.
- the audio feature data is extracted from the audio data, and the audio feature data is input into the dotted voice recognition model to obtain the dotted voice recognition result.
- Step S409 According to the results of the dot-action recognition, the voice recognition results of the dot and the preset trigger rules of the dot, the results of the dot recognition are obtained.
- the results of the dot-recognition are obtained. Specifically, it may include: querying preset preset trigger rules, which include behavior trigger rules and voice trigger rules; comparing the results of the dot hit behavior recognition with the behavior trigger rules to obtain the behavior trigger results; comparing the dot hit speech recognition results with the voice trigger rules Compare to get the voice trigger result; according to the behavior trigger result and the voice trigger result, get the dot recognition result.
- preset preset trigger rules which include behavior trigger rules and voice trigger rules
- comparing the dot hit speech recognition results with the voice trigger rules Compare to get the voice trigger result; according to the behavior trigger result and the voice trigger result, get the dot recognition result.
- Step S410 When the type of the dot recognition result is the operation dot, add a cut point identifier to the video data to be recognized;
- Step S411 Cut the video stream data according to the cut point identifier to obtain video segment data.
- the type is judged.
- the type of dot recognition result is operation dot, it indicates that the video data to be recognized is a cut point, and the dot processing is performed on the video data to be recognized.
- a cut point identifier can be added to the video data to be recognized.
- Step S412 Extract audio segment data from video segment data
- Step S413 query a preset voice recognition model
- Step S414 Input audio segment data into the speech recognition model to obtain translation data of the video segment data;
- Step S415 Determine the service type corresponding to the video segment data according to the translation data, and store the video segment data in a storage location corresponding to the service type.
- the video segment data obtained by cutting the video stream data after the video segment data obtained by cutting the video stream data is obtained, it can be stored in a corresponding storage location according to the service type of each video segment data, so as to realize automatic classification storage of the video segment data.
- steps in the flowcharts of FIGS. 2-4 are displayed in order according to the arrows, the steps are not necessarily executed in the order indicated by the arrows. Unless clearly stated in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least some of the steps in FIGS. 2-4 may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but may be executed at different times. These sub-steps or stages The execution order of is not necessarily sequential, but may be executed in turn or alternately with at least a part of other steps or sub-steps or stages of other steps.
- a video cutting device including: an identification data extraction module 501, a dot recognition processing module 503, a dot result acquisition module 505, a cut identification adding module 507, and a video cutting module 509, where:
- the identification data extraction module 501 is used to extract video data to be identified from video stream data, and extract image data and audio data from the video data to be identified;
- the dot recognition processing module 503 is used to input the image data into the preset dot behavior recognition model to obtain the dot behavior recognition result, and input the audio data into the preset dot behavior recognition model to obtain the dot voice recognition result;
- the hitting result acquisition module 505 is used to obtain the hitting recognition result according to the hitting behavior recognition result, the hitting voice recognition result, and the preset hitting trigger rules;
- a cutting mark adding module 507 which is used to add a cutting point mark to the video data to be recognized when the type of the dot recognition result is the operation dot;
- the video cutting module 509 is used to cut the video stream data according to the cut point identifier to obtain video segment data.
- the identification data extraction module 501 includes a video stream acquisition unit, an identification length determination unit, and an identification data extraction unit, wherein: the video stream acquisition unit is used to acquire video stream data; and the identification length determination unit is used to determine Video stream identification length; and identification data extraction unit for extracting video data to be identified from the video stream data according to the video stream identification length.
- the spot recognition processing module 503 includes an identity identification determination unit, an identification model query unit, a feature data extraction unit, and a spot identification unit, wherein: the identity identification determination unit is used to determine that the video data to be identified corresponds to the business personnel to which it belongs Identification information; identification model query unit for querying identity identification information corresponding to the preset dot behavior recognition model and dot voice recognition model; feature data extraction unit for extracting image feature data from image data and audio data Audio feature data is extracted from; and a dot recognition unit is used to input image feature data into a dot behavior recognition model to obtain a dot behavior recognition result, and input audio feature data into a dot speech recognition model to obtain a dot speech recognition result.
- the identity identification determination unit is used to determine that the video data to be identified corresponds to the business personnel to which it belongs Identification information
- identification model query unit for querying identity identification information corresponding to the preset dot behavior recognition model and dot voice recognition model
- feature data extraction unit for extracting image feature data from image data and audio data Audio feature data is extracted
- it also includes a historical data acquisition module, a historical data classification module, a behavior recognition model training module, and a speech recognition model training module, where: the historical data acquisition module is used to acquire historical behavior image data and Historical dotted voice data; historical data classification module, used to classify historical behavior image data and historical dotted voice data according to business personnel, respectively, to obtain historical behavior image data corresponding to each business personnel and historical dotted voice data corresponding to each business personnel; The behavior recognition model training module is used to train the historical behavior image data corresponding to each business personnel to obtain a dotted behavior recognition model; and the voice recognition model training module is used to train the historical dotted speech data corresponding to each business personnel to obtain a dotted speech recognition model .
- the hitting result acquisition module 505 includes a triggering rule query unit, a behavior comparison unit, a voice comparison unit, and a hitting result acquisition unit, wherein: the triggering rule query unit is used to query preset hitting trigger rules and hitting triggers Rules include behavior triggering rules and voice triggering rules; behavior comparison unit, used to compare the results of dotted behavior recognition and behavior triggering rules to obtain behavior triggering results; voice comparison unit, used to compare the results of dotted speech recognition and voice triggering rules To get the voice trigger result; and the hitting result acquisition unit, which is used to get the hitting recognition result according to the behavior triggering result and the voice triggering result.
- the triggering rule query unit is used to query preset hitting trigger rules and hitting triggers
- Rules include behavior triggering rules and voice triggering rules
- behavior comparison unit used to compare the results of dotted behavior recognition and behavior triggering rules to obtain behavior triggering results
- voice comparison unit used to compare the results of dotted speech recognition and voice triggering rules To get the voice trigger result
- the hitting result acquisition unit which is used to get the hitting recognition result
- it further includes a cutting instruction receiving module, a cutting frame determining module, an identification adding module and a cutting processing module, wherein: the cutting instruction receiving module is used to determine the cutting of the cutting instruction when receiving the cutting instruction Time value; cutting frame determination module, used to determine the cutting time frame corresponding to the cutting time value in the video data to be identified; identification adding module, used to add cutting point identification to the cutting video frame; and cutting processing module, used to return to The cutting point identifier cuts the video stream data to obtain video segment data.
- the audio segment extraction module is used to extract audio segment data from the video segment data
- voice Recognition model query module used to query the preset speech recognition model
- translation data acquisition module used to input audio segment data into the speech recognition model to obtain translated data of video segment data
- video segment storage module used to search for translation The data determines the service type corresponding to the video segment data, and stores the video segment data in the storage location corresponding to the service type.
- Each module in the above video cutting device may be implemented in whole or in part by software, hardware, or a combination thereof.
- the above modules may be embedded in the hardware or independent of the processor in the computer device, or may be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.
- a computer device is provided.
- the computer device may be a server, and its internal structure may be as shown in FIG. 6.
- the computer device includes a processor, memory, and network interface connected by a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities.
- the memory of the computer device includes a non-volatile storage medium and an internal memory.
- the non-volatile storage medium stores an operating system and computer-readable instructions.
- the internal memory provides an environment for the operation of the operating system and computer-readable instructions in the non-volatile storage medium.
- the network interface of the computer device is used to communicate with external terminals through a network connection.
- the computer readable instructions are executed by the processor to implement a video cutting method.
- FIG. 6 is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer equipment to which the solution of the present application is applied.
- the specific computer equipment may It includes more or fewer components than shown in the figure, or some components are combined, or have a different component arrangement.
- a computer device includes a memory and one or more processors.
- the memory stores computer-readable instructions.
- the steps of the video cutting method provided in any embodiment of the present application are implemented.
- One or more non-volatile storage media storing computer-readable instructions.
- the one or more processors implement the video provided in any embodiment of the present application Steps of cutting method.
- Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
- Volatile memory can include random access memory (RAM) or external cache memory.
- RAM random access memory
- DRAM dynamic RAM
- SDRAM synchronous DRAM
- DDRSDRAM double data rate SDRAM
- ESDRAM enhanced SDRAM
- SLDRAM synchronous chain (Synchlink) DRAM
- RDRAM direct RAM
- DRAM direct memory bus dynamic RAM
- RDRAM memory bus dynamic RAM
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Abstract
Description
Claims (20)
- 一种视频切割方法,包括:从视频流数据中提取待识别视频数据,并从所述待识别视频数据中提取影像数据和音频数据;将所述影像数据输入预设的打点行为识别模型中,得到打点行为识别结果,并将所述音频数据输入预设的打点语音识别模型中,得到打点语音识别结果;根据所述打点行为识别结果、所述打点语音识别结果和预设的打点触发规则,得到打点识别结果;当所述打点识别结果的类型为操作打点时,对所述待识别视频数据添加切割点标识;及按照所述切割点标识将所述视频流数据进行切割处理,得到视频段数据。
- 根据权利要求1所述的方法,其特征在于,所述从视频流数据中提取待识别视频数据,包括:获取视频流数据;确定视频流识别长度;及按照所述视频流识别长度,从所述视频流数据中提取待识别视频数据。
- 根据权利要求1所述的方法,其特征在于,所述将所述影像数据输入预设的打点行为识别模型中,得到打点行为识别结果,并将所述音频数据输入预设的打点语音识别模型中,得到打点语音识别结果,包括:确定所述待识别视频数据对应所属业务人员的身份标识信息;查询所述身份标识信息分别对应预设的打点行为识别模型和打点语音识别模型;从所述影像数据中提取影像特征数据,从所述音频数据中提取音频特征数据;及将所述影像特征数据输入所述打点行为识别模型中,得到打点行为识别结果,将所述音频特征数据输入所述打点语音识别模型中,得到打点语音识 别结果。
- 根据权利要求3所述的方法,其特征在于,在所述查询所述身份标识信息分别对应预设的打点行为识别模型和打点语音识别模型之前,所述方法还包括:从业务系统中获取历史行为影像数据和历史打点语音数据;分别将所述历史行为影像数据和所述历史打点语音数据按照业务人员进行分类,得到各业务人员对应的历史行为影像数据和各业务人员对应的历史打点语音数据;训练所述各业务人员对应的历史行为影像数据,得到所述打点行为识别模型;及训练所述各业务人员对应的历史打点语音数据,得到所述打点语音识别模型。
- 根据权利要求1所述的方法,其特征在于,所述根据所述打点行为识别结果、所述打点语音识别结果和预设的打点触发规则,得到打点识别结果,包括:查询预设的打点触发规则,所述打点触发规则包括行为触发规则和语音触发规则;将所述打点行为识别结果与所述行为触发规则进行比较,得到行为触发结果;将所述打点语音识别结果与所述语音触发规则进行比较,得到语音触发结果;及根据所述行为触发结果和所述语音触发结果,得到打点识别结果。
- 根据权利要求5所述的方法,其特征在于,所述根据所述行为触发结果和所述语音触发结果,得到打点识别结果,包括:对所述行为触发结果和所述语音触发结果进行取或运算,得到打点识别结果。
- 根据权利要求1所述的方法,其特征在于,所述当所述打点识别结果 的类型为操作打点时,对所述待识别视频数据添加切割点标识,包括:确定所述打点识别结果的类型;当所述打点识别结果的类型为操作打点时,查询预设的标签添加规则;根据所述标签添加规则,从所述待识别视频数据中确定关键帧,并为所述关键帧添加切割标签,所述切割点标识包括所述切割标签。
- 根据权利要求1至7任意一项所述的方法,其特征在于,还包括:当接收到打点切割指令时,确定所述打点切割指令的切割时刻值;确定所述切割时刻值在所述待识别视频数据中对应的切割视频帧;为所述切割视频帧添加切割点标识;及返回所述按照所述切割点标识将所述视频流数据进行切割处理,得到视频段数据。
- 根据权利要求8所述的方法,其特征在于,在所述得到视频段数据之后,所述方法还包括:从所述视频段数据中提取音频段数据;查询预设的语音识别模型;将所述音频段数据输入所述语音识别模型中,得到所述视频段数据的译文数据;及根据所述译文数据确定所述视频段数据对应的业务类型,并将所述视频段数据存储至所述业务类型对应的存储位置中。
- 根据权利要求9所述的方法,其特征在于,所述根据所述译文数据确定所述视频段数据对应的业务类型,并将所述视频段数据存储至所述业务类型对应的存储位置中,包括:从所述译文数据中提取业务关键字;根据所述业务关键字确定所述视频段数据对应的业务类型;查询所述业务类型对应预设的存储位置;将所述视频段数据存储至所述存储位置中。
- 一种视频切割装置,包括:识别数据提取模块,用于从视频流数据中提取待识别视频数据,并从所述待识别视频数据中提取影像数据和音频数据;打点识别处理模块,用于将所述影像数据输入预设的打点行为识别模型中,得到打点行为识别结果,并将所述音频数据输入预设的打点语音识别模型中,得到打点语音识别结果;打点结果获取模块,用于根据所述打点行为识别结果、所述打点语音识别结果和预设的打点触发规则,得到打点识别结果;切割标识添加模块,用于当所述打点识别结果的类型为操作打点时,对所述待识别视频数据添加切割点标识;及视频切割模块,用于按照所述切割点标识将所述视频流数据进行切割处理,得到视频段数据。
- 根据权利要求11所述的装置,其特征在于,所述识别数据提取模块,包括:视频流获取单元,用于获取视频流数据;识别长度确定单元,用于确定视频流识别长度;及识别数据提取单元,用于按照所述视频流识别长度,从所述视频流数据中提取待识别视频数据。
- 根据权利要求11所述的装置,其特征在于,所述打点识别处理模块,包括:身份标识确定单元,用于确定所述待识别视频数据对应所属业务人员的身份标识信息;识别模型查询单元,用于查询所述身份标识信息分别对应预设的打点行为识别模型和打点语音识别模型;特征数据提取单元,用于从所述影像数据中提取影像特征数据,从所述音频数据中提取音频特征数据;及打点识别单元,用于将所述影像特征数据输入所述打点行为识别模型中,得到打点行为识别结果,将所述音频特征数据输入所述打点语音识别模型中, 得到打点语音识别结果。
- 根据权利要求13所述的装置,其特征在于,所述装置还包括:历史数据获取模块,用于从业务系统中获取历史行为影像数据和历史打点语音数据;历史数据分类模块,用于分别将所述历史行为影像数据和所述历史打点语音数据按照业务人员进行分类,得到各业务人员对应的历史行为影像数据和各业务人员对应的历史打点语音数据;行为识别模型训练模块,用于训练所述各业务人员对应的历史行为影像数据,得到所述打点行为识别模型;及语音识别模型训练模块,用于训练所述各业务人员对应的历史打点语音数据,得到所述打点语音识别模型。
- 一种计算机设备,包括存储器及一个或多个处理器,所述存储器中储存有计算机可读指令,所述计算机可读指令被所述一个或多个处理器执行时,使得所述一个或多个处理器执行以下步骤:从视频流数据中提取待识别视频数据,并从所述待识别视频数据中提取影像数据和音频数据;将所述影像数据输入预设的打点行为识别模型中,得到打点行为识别结果,并将所述音频数据输入预设的打点语音识别模型中,得到打点语音识别结果;根据所述打点行为识别结果、所述打点语音识别结果和预设的打点触发规则,得到打点识别结果;当所述打点识别结果的类型为操作打点时,对所述待识别视频数据添加切割点标识;及按照所述切割点标识将所述视频流数据进行切割处理,得到视频段数据。
- 根据权利要求15所述的计算机设备,其特征在于,所述处理器执行所述计算机可读指令时还执行以下步骤:获取视频流数据;确定视频流识别长度;及按照所述视频流识别长度,从所述视频流数据中提取待识别视频数据。
- 根据权利要求15所述的计算机设备,其特征在于,所述处理器执行所述计算机可读指令时还执行以下步骤:确定所述待识别视频数据对应所属业务人员的身份标识信息;查询所述身份标识信息分别对应预设的打点行为识别模型和打点语音识别模型;从所述影像数据中提取影像特征数据,从所述音频数据中提取音频特征数据;及将所述影像特征数据输入所述打点行为识别模型中,得到打点行为识别结果,将所述音频特征数据输入所述打点语音识别模型中,得到打点语音识别结果。
- 一个或多个存储有计算机可读指令的非易失性计算机可读存储介质,所述计算机可读指令被一个或多个处理器执行时,使得所述一个或多个处理器执行以下步骤:从视频流数据中提取待识别视频数据,并从所述待识别视频数据中提取影像数据和音频数据;将所述影像数据输入预设的打点行为识别模型中,得到打点行为识别结果,并将所述音频数据输入预设的打点语音识别模型中,得到打点语音识别结果;根据所述打点行为识别结果、所述打点语音识别结果和预设的打点触发规则,得到打点识别结果;当所述打点识别结果的类型为操作打点时,对所述待识别视频数据添加切割点标识;及按照所述切割点标识将所述视频流数据进行切割处理,得到视频段数据。
- 根据权利要求18所述的存储介质,其特征在于,所述计算机可读指令被所述处理器执行时还执行以下步骤:获取视频流数据;确定视频流识别长度;及按照所述视频流识别长度,从所述视频流数据中提取待识别视频数据。
- 根据权利要求18所述的存储介质,其特征在于,所述计算机可读指令被所述处理器执行时还执行以下步骤:确定所述待识别视频数据对应所属业务人员的身份标识信息;查询所述身份标识信息分别对应预设的打点行为识别模型和打点语音识别模型;从所述影像数据中提取影像特征数据,从所述音频数据中提取音频特征数据;及将所述影像特征数据输入所述打点行为识别模型中,得到打点行为识别结果,将所述音频特征数据输入所述打点语音识别模型中,得到打点语音识别结果。
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EP3890333A4 (en) | 2022-05-25 |
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