CN113297422A - Data processing method, data processing apparatus, electronic device, medium, and program product - Google Patents
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Abstract
The embodiment of the disclosure discloses a data processing method, a data processing device, an electronic device, a medium and a program product, wherein the method comprises the following steps: acquiring object information of a specified object from specified data related to video data; determining a video clip related to the specified object in the video data according to the object information of the specified object; an output video is generated based on the video segments.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a data processing method, an apparatus, an electronic device, a medium, and a program product.
Background
With the rise of live broadcast, the live broadcast is more hot in tourism. The live tour can present the destination and tour route to the audience in a complete and intuitive manner. However, the original live video for tourism has a long time, generally several tens of minutes as short as several hours as long as the original live video for tourism, and meanwhile, the original live video for tourism usually has one mirror to the bottom and often contains some lens information irrelevant to tourist attractions. How to extract the essential information in the live video of tourism, cut it into the short film to let spectator can obtain all information fast in the short time, be the key that the live video of tourism carries out secondary content production.
Disclosure of Invention
In order to solve the problems in the related art, embodiments of the present disclosure provide a data processing method, an apparatus, an electronic device, a medium, and a program product.
In a first aspect, an embodiment of the present disclosure provides a data processing method, including:
acquiring object information of a specified object from specified data related to video data;
determining a video clip related to the specified object in the video data according to the object information of the specified object;
an output video is generated based on the video segments.
With reference to the first aspect, in a first implementation manner of the first aspect, the specific data includes one or more of the following data: text data, picture data, audio data, the object information of the specified object is obtained from specified data related to the video data, including:
extracting a name of a specified object from the text data; and/or
Extracting a name of a specified object from the audio data; and/or
An image of a specified object is acquired from the picture data.
With reference to the first implementation manner of the first aspect, in a second implementation manner of the first aspect, the determining, according to the object information of the specified object, a video segment related to the specified object in the video data includes:
carrying out object identification on the video data to obtain a first video segment matched with the name of the specified object; and/or
And matching the image of the specified object with the video data to obtain a second video clip matched with the image of the specified object.
With reference to the second implementation manner of the first aspect, in a third implementation manner of the first aspect, the matching the image of the specified object with the video data to obtain a second video segment matched with the image of the specified object includes:
performing main body detection on the image of the specified object to obtain a first detection result;
performing main body detection on the video data to obtain a second detection result;
and obtaining the second video clip according to the similarity between the first detection result and the second detection result.
With reference to the third implementation manner of the first aspect, in a fourth implementation manner of the first aspect, the calculating the similarity between the first detection result and the second detection result includes:
determining the first similarity based on a cosine distance between image features of the first detection result and the second detection result; and/or
Calculating a first differential hash code of the first detection result and a second differential hash code of the second detection result, and determining the second similarity based on a hamming distance between the first differential code and the second differential code.
With reference to the third implementation manner of the first aspect, in a fifth implementation manner of the first aspect, the generating an output video based on the video segment includes:
obtaining the confidence of each video clip in the first video clip and the second video clip;
and generating the output video based on the video clips with the confidence degrees meeting the preset conditions.
With reference to the second implementation manner of the first aspect, in a sixth implementation manner of the first aspect, the generating an output video based on the video segment includes any one of:
generating the output video based on the first video segment;
generating the output video based on the second video segment;
generating the output video based on the first video segment and the second video segment;
generating the output video based on an intersection of the first video segment and the second video segment.
With reference to the first aspect, the present disclosure provides in a seventh implementation manner of the first aspect:
the designated object comprises a sight spot, the text data is a text in sight spot tourist notes or sight spot introduction, and the picture data is an image in sight spot tourist notes or sight spot introduction; or
The specified object comprises a product, the text data is text in a product description page, and the picture data is an image in the product description page.
With reference to the first aspect, in an eighth implementation manner of the first aspect, the method further includes:
acquiring the specified data according to any one or more of the following video data: title, abstract, keywords.
With reference to the first aspect, in a ninth implementation manner of the first aspect, the method further includes:
and synthesizing the part related to the output video in the specified data and the output video.
In a second aspect, an embodiment of the present disclosure provides a data processing method, including:
transmitting video data containing a specified object;
receiving output video information for obtaining an output video automatically generated based on a video clip of the video data.
With reference to the second aspect, in a first implementation manner of the second aspect, the video clip is a video clip related to a specific object obtained from video data according to object information of the specific object obtained from the specific data, and the specific data includes one or more of the following data: text data, picture data, audio data.
With reference to the first implementation manner of the second aspect, the present disclosure provides in a second implementation manner of the second aspect:
the designated object is a sight spot, the text data is a text in sight spot travel notes or sight spot introductions, and the picture data is an image in sight spot travel notes or sight spot introductions; or
The specified object is a product, the text data is text in a product description, and the picture data is an image in the product description.
In a third aspect, an embodiment of the present disclosure provides a data processing apparatus, including:
a first acquisition module configured to acquire object information of a specified object from specified data related to video data;
a determining module configured to determine a video segment related to the specified object in the video data according to the object information of the specified object;
a generation module configured to generate an output video based on the video segments.
With reference to the third aspect, in a first implementation manner of the third aspect, the specifying data includes one or more of the following data: text data, picture data, audio data, the object information of the specified object is obtained from specified data related to the video data, including:
extracting a name of a specified object from the text data; and/or
Extracting a name of a specified object from the audio data; and/or
An image of a specified object is acquired from the picture data.
With reference to the first implementation manner of the third aspect, the present disclosure provides in a second implementation manner of the third aspect, the determining, according to the object information of the specified object, a video segment related to the specified object in the video data includes:
carrying out object identification on the video data to obtain a first video segment matched with the name of the specified object; and/or
And matching the image of the specified object with the video data to obtain a second video clip matched with the image of the specified object.
With reference to the second implementation manner of the third aspect, in a third implementation manner of the third aspect, the matching the image of the specified object with the video data to obtain a second video segment matched with the image of the specified object includes:
performing main body detection on the image of the specified object to obtain a first detection result;
performing main body detection on the video data to obtain a second detection result;
and obtaining the second video clip according to the similarity between the first detection result and the second detection result.
With reference to the third implementation manner of the third aspect, in a fourth implementation manner of the third aspect, the calculating the similarity between the first detection result and the second detection result includes:
determining the first similarity based on a cosine distance between image features of the first detection result and the second detection result; and/or
Calculating a first differential hash code of the first detection result and a second differential hash code of the second detection result, and determining the second similarity based on a hamming distance between the first differential code and the second differential code.
With reference to the third implementation manner of the third aspect, in a fifth implementation manner of the third aspect, the generating an output video based on the video clip includes:
obtaining the confidence of each video clip in the first video clip and the second video clip;
and generating the output video based on the video clips with the confidence degrees meeting the preset conditions.
With reference to the second implementation manner of the third aspect, in a sixth implementation manner of the third aspect, the generating an output video based on the video segment includes any one of:
generating the output video based on the first video segment;
generating the output video based on the second video segment;
generating the output video based on the first video segment and the second video segment;
generating the output video based on an intersection of the first video segment and the second video segment.
With reference to the third aspect, the present disclosure provides in a seventh implementation manner of the third aspect:
the designated object comprises a sight spot, the text data is a text in sight spot tourist notes or sight spot introduction, and the picture data is an image in sight spot tourist notes or sight spot introduction; or
The specified object comprises a product, the text data is text in a product description page, and the picture data is an image in the product description page.
With reference to the third aspect, in an eighth implementation manner of the third aspect, the apparatus further includes:
a second obtaining module configured to obtain the specified data according to any one or more of the following of the video data: title, abstract, keywords.
With reference to the third aspect, in a ninth implementation manner of the third aspect, the apparatus further includes:
a synthesizing module configured to synthesize the portion of the specified data related to the output video with the output video.
In a fourth aspect, an embodiment of the present disclosure provides a data processing apparatus, including:
a transmission module configured to transmit video data containing a specified object;
a receiving module configured to receive output video information for acquiring an output video automatically generated based on a video clip of the video data.
With reference to the fourth aspect, in a first implementation manner of the fourth aspect, the specifying data includes one or more of the following data: text data, picture data, audio data, the video clip is a video clip related to a specified object obtained from the video data according to the object information of the specified object obtained from the specified data.
With reference to the first implementation manner of the fourth aspect, the present disclosure provides in a second implementation manner of the fourth aspect:
the designated object is a sight spot, the text data is a text in sight spot travel notes or sight spot introductions, and the picture data is an image in sight spot travel notes or sight spot introductions; or
The specified object is a product, the text data is text in a product description, and the picture data is an image in the product description.
In a fifth aspect, the present disclosure provides an electronic device, including a memory and a processor, where the memory is configured to store one or more computer instructions, where the one or more computer instructions are executed by the processor to implement the method according to any one of the first to second implementation manners of the first aspect.
In a sixth aspect, an embodiment of the present disclosure provides a computer-readable storage medium having stored thereon computer instructions, which, when executed by a processor, implement the method according to any one of the second implementation manners of the first aspect to the second aspect.
In a seventh aspect, this disclosed embodiment provides a computer program product comprising computer instructions that, when executed by a processor, implement the method steps as described in any one of the second implementation manners of the first aspect to the second aspect.
According to the technical scheme provided by the embodiment of the disclosure, object information of a specified object is acquired from specified data, a video clip related to the specified object in video data is determined according to the object information of the specified object, and an output video is generated based on the video clip. According to the embodiment of the disclosure, the information of the designated object is acquired based on the designated data, the video segment related to the designated object is automatically determined in the video data according to the information of the designated object, and the output video is generated, so that the fast and efficient intelligent automatic editing of the video is realized, the batch full-automatic short video production can be performed, and more new technical improvements are brought to the secondary content production and distribution of the live video.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
Other features, objects, and advantages of the present disclosure will become more apparent from the following detailed description of non-limiting embodiments when taken in conjunction with the accompanying drawings. In the drawings:
fig. 1 shows a flow diagram of a data processing method according to an embodiment of the present disclosure.
Fig. 2 shows a flow chart of extracting video clips from a travel live video to generate a short video according to an embodiment of the disclosure.
Fig. 3 shows a flowchart for calculating the similarity between the first detection result and the second detection result.
FIG. 4 shows a flow diagram of a data processing method according to an embodiment of the present disclosure.
Fig. 5A illustrates a block diagram of a data processing apparatus according to an embodiment of the present disclosure.
Fig. 5B shows a block diagram of a data processing apparatus according to an embodiment of the present disclosure.
Fig. 6 shows a block diagram of an electronic device according to an embodiment of the present disclosure.
FIG. 7 shows a schematic block diagram of a computer system suitable for use in implementing a method according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. Also, for the sake of clarity, parts not relevant to the description of the exemplary embodiments are omitted in the drawings.
In the present disclosure, it is to be understood that terms such as "including" or "having," etc., are intended to indicate the presence of the disclosed features, numbers, steps, behaviors, components, parts, or combinations thereof, and are not intended to preclude the possibility that one or more other features, numbers, steps, behaviors, components, parts, or combinations thereof may be present or added.
It should be further noted that the embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
In the present disclosure, the acquisition of the user information or the user data is an operation that is authorized, confirmed, or actively selected by the user.
As described above, the original live video for tourism has a long time, generally the short time is tens of minutes, and the long time is hours, meanwhile, the original live video is generally a mirror to the bottom, and often contains some lens information unrelated to tourist attractions. How to extract the essential information in the live video of tourism, cut it into the short film to let spectator can obtain all information fast in the short time, be the key that the live video of tourism carries out secondary content production.
In the current live video clip for tourism, most of the live video clips are clipped to generate short videos based on an artificial punctuation method, so that the cost is high, the efficiency is low, and the requirement for processing massive videos is difficult to meet.
The embodiment of the disclosure provides a data processing method, which includes: acquiring object information of a specified object from specified data; determining a video clip related to the specified object in the video data according to the object information of the specified object; an output video is generated based on the video segments. According to the embodiment of the disclosure, the information of the designated object is acquired based on the designated data, the video segment related to the designated object is automatically determined in the video data according to the information of the designated object, and the output video is generated, so that the fast and efficient intelligent automatic editing of the video is realized, the batch full-automatic short video production can be performed, and more new technical improvements are brought to the secondary content production and distribution of the live video.
Fig. 1 shows a flow diagram of a data processing method according to an embodiment of the present disclosure. As shown in fig. 1, the data processing method includes the following steps S101 to S103:
in step S101, object information of a specified object is acquired from specified data related to video data;
in step S102, determining a video segment related to the specified object in the video data according to the object information of the specified object;
in step S103, an output video is generated based on the video clip.
According to the embodiment of the disclosure, the information of the designated object is acquired based on the designated data, the video segment related to the designated object is automatically determined in the video data according to the information of the designated object, and the output video is generated, so that the fast and efficient intelligent automatic editing of the video is realized, the batch full-automatic short video production can be performed, and more new technical improvements are brought to the secondary content production and distribution of the live video.
According to an embodiment of the present disclosure, the specified data includes one or more of: text data, picture data, audio data.
According to the embodiment of the disclosure, the specified data related to the video data is acquired according to any one or more of the following items of the video data: title, abstract, keywords.
According to the embodiment of the disclosure, the designated object comprises a sight spot, the text data is a text in sight spot travel notes or sight spot introduction, and the picture data is an image in sight spot travel notes or sight spot introduction. Or, the specified object includes a product, the text data is text in a product description page, and the picture data is an image in the product description page.
For convenience of description, the following description is given by taking a designated object as a sight spot, text data is text in sight spot travel notes or sight spot introductions, picture data is images in sight spot travel notes or sight spot introductions, audio data is sight spot travel note audio or sight spot introduction audio, and video data is live travel video as an exemplary application scene. It will be appreciated that the disclosed embodiments are not limited to this application scenario, but may also be used for the processing of video data relating to a product or other designated object.
Fig. 2 shows a flow chart of extracting video clips from a travel live video to generate a short video according to an embodiment of the disclosure.
According to an embodiment of the present disclosure, the acquiring object information of the specified object from the specified data includes: extracting a name of a specified object from the text data; and/or extracting a name of a specified object from the audio data; and/or acquiring an image of a specified object from the picture data.
For example, as shown in FIG. 2, the designated object is a sight, the sight name may be obtained from the text and/or audio of the sight inscription, and the sight image may be obtained from the image of the sight inscription. Specifically, the sight spot travel notes include a large amount of text content, and generally include sight spot names, such as "Tiananmen", "water cube", and the like, and the sight spot names can be acquired from the sight spot travel notes in a text recognition manner as object information. On the other hand, the sight spot tourist records generally include images (such as photos and short videos) shot by the tourist, the images may include images of sight spots, and the images shot by the tourist can be subjected to image recognition, and the sight spot images are used as object information. The scenic spot names can also appear in the audio of the scenic spot tourist notes or the scenic spot introductions, and the names of the scenic spots can be identified from the audio of the scenic spot tourist notes or the scenic spot introductions in a voice identification mode.
According to an embodiment of the present disclosure, the determining, according to the object information of the specified object, a video segment related to the specified object in video data includes: carrying out object identification on the video data to obtain a first video segment matched with the name of the specified object; and/or matching the image of the specified object with the video data to obtain a second video segment matched with the image of the specified object.
For example, as shown in fig. 2, after the sight spot name is obtained, sight spot identification is performed on a live travel video, the identification result is matched with the sight spot name extracted from the sight spot travel notes, and a video clip including the sight spot matched with the sight spot name can be obtained as a first video clip.
On the other hand, a video clip matched with the sight spot image in the live tourism video can be obtained through an image similarity matching algorithm and serves as a second video clip.
According to an embodiment of the present disclosure, the generating an output video based on the video segment includes any one of: generating the output video based on the first video segment; generating the output video based on the second video segment; generating the output video based on the first video segment and the second video segment; generating the output video based on an intersection of the first video segment and the second video segment.
For example, the sight names extracted from the sight biographies include "Tiananmen," water cube, "and" bird nest, "then the first video segment may include a video segment identifying" Tiananmen, "a video segment identifying" water cube, "and a video segment identifying" bird nest. On the other hand, the sight spot images obtained from the sight spot travel notes comprise images of the Tiananmen, the water cube and the national theater, and the video segments similar to the images of the Tiananmen, the water cube and the national theater can be obtained from the live travel videos through an image similarity matching algorithm.
The output video may then be generated based on the first video segment, or based on the second video segment, or based on both the first video segment and the second video segment.
According to an embodiment of the present disclosure, a name of a specified object may be acquired from text data, a first video segment may be determined based on the name of the specified object, and an output video may be generated based on the first video segment. Alternatively, an image of the specified object may be acquired from the picture data, a second video segment may be determined based on the image of the specified object, and the output video may be generated based on the second video segment. In this way, the video segments containing the specified objects can be intelligently and automatically determined from the video data and the output video is generated, so that the fast and efficient intelligent automatic clipping of the video is realized.
According to an embodiment of the present disclosure, the output video may be generated based on the first video segment and the second video segment. Because the text data may not contain all the sight spot names and the image data may not contain all the sight spot images, the omission of the sight spots can be effectively avoided by generating the output video based on the first video clip and the second video clip.
According to an embodiment of the present disclosure, the output video may be generated based on an intersection of the first video segment and the second video segment.
For example, if the intersection of the first video segment and the second video segment includes a video segment containing Tiananmen and a video segment containing water cubes, the output video may be generated based on the video segment containing Tiananmen and the video segment containing water cubes. Since the recognition results of the first video segment and the second video segment may be wrong, for example, the a sight is mistaken for the B sight, the output video is generated based on the intersection of the first video segment and the second video segment, and the accuracy of the processing result can be improved. For example, if the first video clip and the second video clip both contain a video clip, then there is a greater probability that the video clip is the desired sight video clip.
According to an embodiment of the present disclosure, the matching the image of the designated object with the video data to obtain a second video segment matched with the image of the designated object includes: performing main body detection on the image of the specified object to obtain a first detection result; performing main body detection on the video data to obtain a second detection result; and obtaining the second video clip according to the similarity between the first detection result and the second detection result.
According to an embodiment of the present disclosure, the detection result includes an image of the subject obtained by the detection.
For example, in fig. 2, a saliency detection algorithm is used to perform main body detection on an image of a scene to obtain a first detection result, a saliency detection algorithm is used to perform main body detection on a video frame to obtain a second detection result, and a similarity is calculated for the first detection result and the second detection result. And if the similarity between the first detection result of the video frame and the second detection result of the scenery spot image is higher than a preset threshold value, matching the video frame with the scenery spot image, and combining the video frames matched with the scenery spot image to obtain a second video clip.
According to an embodiment of the present disclosure, the calculating a similarity between the first detection result and the second detection result includes: based on a cosine distance between image features of the first detection result and the second detection result as the first similarity; and/or calculating a first differential hash code of the first detection result and a second differential hash code of the second detection result, and determining the second similarity based on a hamming distance between the first differential code and the second differential code.
Fig. 3 shows a flowchart for calculating the similarity between the first detection result and the second detection result.
As shown in fig. 3, a plurality of video frames can be obtained from a live travel video, a first detection result is obtained by performing main body detection on the video frames, and a second detection result is obtained by performing main body detection on the sight spot images. And inputting the first detection result and the second detection result into the VGG model respectively to extract the image characteristics of the first detection result and the image characteristics of the second detection result. The VGG model is a convolutional network model by which features of an image can be extracted. Then, the first similarity is determined based on a cosine distance between the image feature of the first detection result and the image feature of the second detection result. Cosine distance is a way of measuring the distance between features, with a larger distance indicating a larger phase difference. Therefore, the reciprocal of the cosine distance between the image feature of the first detection result and the image feature of the second detection result can be taken as the first similarity, and the greater the first similarity, the higher the matching degree between the first detection result and the second detection result, the more similar the two are.
As shown in fig. 3, a first differential hash (Dhash) code of a first detection result and a second differential hash code of a second detection result may also be calculated, and the second similarity may be determined based on a Hamming Distance (Hamming Distance) between the first differential code and the second differential code. Hamming distance is a way to measure the distance between features, with larger distances indicating larger phase differences. Therefore, the reciprocal of the hamming distance between the first differential encoding and the second differential encoding can be taken as the second similarity, and the greater the second similarity, the higher the matching degree between the first detection result and the second detection result, the more similar the two are.
According to the embodiment of the disclosure, the video frame matched with the sight image can be selected according to the first similarity and/or the second similarity. For example, when the first similarity is above a first preset threshold, the sight image may be considered to match the video frame. Alternatively, when the second similarity is higher than a second preset threshold, the sight image may be considered to match the video frame. Alternatively, when the first similarity is higher than a first preset threshold and the second similarity is higher than a second preset threshold, the sight image may be considered to be matched with the video frame.
According to an embodiment of the present disclosure, the generating an output video based on the video clip includes: obtaining the confidence of each video clip in the first video clip and the second video clip; and generating the output video based on the video clips with the confidence degrees meeting the preset conditions.
For example, the probability that each of the first video segments contains the designated object may be used as the confidence of the first video segment, the average or minimum similarity between the first detection result of each of the video frames in the second video segment and the second detection result of the sight point image may be used as the confidence of the video segment, and the output video may be generated based on the segments with the confidence higher than the third preset threshold in the first video segment and the video segments with the confidence higher than the fourth preset threshold in the second video segment. By selecting the video segment for generating the output video according to the confidence, the accuracy of the processing result can be improved.
According to the embodiment of the present disclosure, after the video segments used for generating the output video are acquired, post-processing may be performed, such as removing jittered and unclear portions of the picture, adding transitions, background music, and the like, to obtain the output video.
According to an embodiment of the present disclosure, a portion of the specification data related to the output video may be further composited with the output video. For example, text portions of the text data that are associated with the output video, such as text portions describing the specified objects, may be extracted and composited with the video segment of the output video in which the corresponding specified object appears, for example, in the form of subtitles. For another example, audio portions of the audio data related to the output video, such as audio portions describing the specified objects, may be extracted and composited with video segments of the output video in which the corresponding specified objects appear, for example, in the form of voice-over or the like. Alternatively, the text may be identified from the audio portions and then composited with the video segment in the output video in which the corresponding designated object appears. For another example, an audio portion of the picture data related to the output video, such as a picture in which the specified object appears, may be extracted and displayed in superimposition with a video clip in the output video in which the corresponding specified object appears. In this way, richer information about the designated object can be provided in the output video, facilitating the viewer to know the designated object more comprehensively and deeply.
FIG. 4 shows a flow diagram of a data processing method according to an embodiment of the present disclosure. As shown in fig. 4, the data processing method includes the following steps S401 to S402:
in step S401, video data containing a specified object is transmitted;
in step S402, output video information for acquiring an output video automatically generated based on a video clip of the video data is received.
The method of fig. 4 may be implemented at a user terminal according to an embodiment of the present disclosure. For example, a user may send video data containing a specified object, such as a travel live video, to a server, which obtains specified data related to the video data, performs the above-described method, generates an output video, and sends the output video information to a user terminal. According to the embodiment of the disclosure, the output video information is used for acquiring the output video automatically generated based on the video clip of the video data, for example, the server may store the output video in a cloud, and the output video information may be a network link pointing to the output video. Alternatively, the server may send the output video to the user terminal, in which case the output video information may be data compression packets or multimedia data streams, etc.
According to an embodiment of the present disclosure, the video clip is a video clip related to a specified object obtained from video data according to object information of the specified object obtained from the specified data.
According to the embodiment of the disclosure, the designated object is a sight spot, the text data is a text in sight spot travel notes or sight spot introduction, and the picture data is an image in sight spot travel notes or sight spot introduction; or the specified object is a product, the text data is text in a product description, and the picture data is an image in the product description.
Fig. 5A illustrates a block diagram of a data processing apparatus according to an embodiment of the present disclosure. The apparatus may be implemented as part or all of an electronic device through software, hardware, or a combination of both.
As shown in fig. 5A, the data processing apparatus 510 includes a first obtaining module 511, a determining module 512, and a generating module 513.
The first acquisition module 511 is configured to acquire object information of a specified object from specified data related to video data;
the determining module 512 is configured to determine a video segment related to the specified object in the video data according to the object information of the specified object;
the generation module 513 is configured to generate an output video based on the video segments.
According to an embodiment of the present disclosure, the data processing device 510 may be implemented on a server side, for example.
According to an embodiment of the present disclosure, wherein the specified data includes one or more of: text data, picture data, audio data, the object information of the specified object is obtained from specified data related to the video data, including:
extracting a name of a specified object from the text data; and/or
Extracting a name of a specified object from the audio data; and/or
An image of a specified object is acquired from the picture data.
According to an embodiment of the present disclosure, the determining, according to the object information of the specified object, a video segment related to the specified object in video data includes:
carrying out object identification on the video data to obtain a first video segment matched with the name of the specified object; and/or
And matching the image of the specified object with the video data to obtain a second video clip matched with the image of the specified object.
According to an embodiment of the present disclosure, the matching the image of the designated object with the video data to obtain a second video segment matched with the image of the designated object includes:
performing main body detection on the image of the specified object to obtain a first detection result;
performing main body detection on the video data to obtain a second detection result;
and obtaining the second video clip according to the similarity between the first detection result and the second detection result.
According to an embodiment of the present disclosure, the calculating a similarity between the first detection result and the second detection result includes:
determining the first similarity based on a cosine distance between image features of the first detection result and the second detection result; and/or
Calculating a first differential hash code of the first detection result and a second differential hash code of the second detection result, and determining the second similarity based on a hamming distance between the first differential code and the second differential code.
According to an embodiment of the present disclosure, the generating an output video based on the video segment includes:
obtaining the confidence of each video clip in the first video clip and the second video clip;
and generating the output video based on the video clips with the confidence degrees meeting the preset conditions.
According to an embodiment of the present disclosure, wherein the generating an output video based on the video segment includes any one of:
generating the output video based on the first video segment;
generating the output video based on the second video segment;
generating the output video based on the first video segment and the second video segment;
generating the output video based on an intersection of the first video segment and the second video segment.
According to an embodiment of the present disclosure, wherein:
the designated object comprises a sight spot, the text data is a text in sight spot tourist notes or sight spot introduction, and the picture data is an image in sight spot tourist notes or sight spot introduction; or
The specified object comprises a product, the text data is text in a product description page, and the picture data is an image in the product description page.
According to an embodiment of the present disclosure, the apparatus 510 further comprises: a second obtaining module 514 configured to obtain the specified data according to any one or more of the following of the video data: title, abstract, keywords.
According to an embodiment of the present disclosure, the data processing apparatus 510 further includes: a composition module 515 configured to compose the portion of the specified data related to the output video with the output video.
Fig. 5B shows a block diagram of a data processing apparatus according to an embodiment of the present disclosure. The apparatus may be implemented as part or all of an electronic device through software, hardware, or a combination of both.
As shown in fig. 5B, the data processing device 520 includes a transmitting module 521 and a receiving module 522.
The transmission module 521 is configured to transmit video data containing a specified object;
the receiving module 522 is configured to receive output video information for obtaining an output video automatically generated based on a video clip of the video data.
According to an embodiment of the present disclosure, the video clip is a video clip related to a specified object obtained from video data according to object information of the specified object obtained from the specified data.
According to an embodiment of the present disclosure, wherein:
the designated object is a sight spot, the text data is a text in sight spot travel notes or sight spot introductions, and the picture data is an image in sight spot travel notes or sight spot introductions; or
The specified object is a product, the text data is text in a product description, and the picture data is an image in the product description.
The present disclosure also discloses an electronic device, and fig. 6 shows a block diagram of the electronic device according to an embodiment of the present disclosure.
As shown in fig. 6, the electronic device 600 includes a memory 601 and a processor 602, wherein the memory 601 is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor 602 to implement a method according to an embodiment of the disclosure.
According to an embodiment of the present disclosure, a data processing method includes:
acquiring object information of a specified object from specified data related to video data;
determining a video clip related to the specified object in the video data according to the object information of the specified object;
an output video is generated based on the video segments.
According to an embodiment of the present disclosure, wherein the specified data includes one or more of: text data, picture data, audio data, the object information of the specified object is obtained from specified data related to the video data, including:
extracting a name of a specified object from the text data; and/or
Extracting a name of a specified object from the audio data; and/or
An image of a specified object is acquired from the picture data.
According to an embodiment of the present disclosure, the determining, according to the object information of the specified object, a video segment related to the specified object in video data includes:
carrying out object identification on the video data to obtain a first video segment matched with the name of the specified object; and/or
And matching the image of the specified object with the video data to obtain a second video clip matched with the image of the specified object.
According to an embodiment of the present disclosure, the matching the image of the designated object with the video data to obtain a second video segment matched with the image of the designated object includes:
performing main body detection on the image of the specified object to obtain a first detection result;
performing main body detection on the video data to obtain a second detection result;
and obtaining the second video clip according to the similarity between the first detection result and the second detection result.
According to an embodiment of the present disclosure, the calculating a similarity between the first detection result and the second detection result includes:
determining the first similarity based on a cosine distance between image features of the first detection result and the second detection result; and/or
Calculating a first differential hash code of the first detection result and a second differential hash code of the second detection result, and determining the second similarity based on a hamming distance between the first differential code and the second differential code.
According to an embodiment of the present disclosure, the generating an output video based on the video segment includes:
obtaining the confidence of each video clip in the first video clip and the second video clip;
and generating the output video based on the video clips with the confidence degrees meeting the preset conditions.
According to an embodiment of the present disclosure, wherein the generating an output video based on the video segment includes any one of:
generating the output video based on the first video segment;
generating the output video based on the second video segment;
generating the output video based on the first video segment and the second video segment;
generating the output video based on an intersection of the first video segment and the second video segment.
According to an embodiment of the present disclosure, wherein:
the designated object comprises a sight spot, the text data is a text in sight spot tourist notes or sight spot introduction, and the picture data is an image in sight spot tourist notes or sight spot introduction; or
The specified object comprises a product, the text data is text in a product description page, and the picture data is an image in the product description page.
According to an embodiment of the present disclosure, the method further comprises:
acquiring the specified data according to any one or more of the following video data: title, abstract, keywords.
According to an embodiment of the present disclosure, the method further comprises:
and synthesizing the part related to the output video in the specified data and the output video.
The embodiment of the disclosure provides a data processing method, which includes:
transmitting video data containing a specified object;
receiving output video information for obtaining an output video automatically generated based on a video clip of the video data.
According to an embodiment of the present disclosure, the video clip is a video clip related to a specific object obtained from video data according to object information of the specific object obtained from the specific data, and the specific data includes one or more of the following data: text data, picture data, audio data.
According to an embodiment of the present disclosure, wherein:
the designated object is a sight spot, the text data is a text in sight spot travel notes or sight spot introductions, and the picture data is an image in sight spot travel notes or sight spot introductions; or
The specified object is a product, the text data is text in a product description, and the picture data is an image in the product description.
FIG. 7 shows a schematic block diagram of a computer system suitable for use in implementing a method according to an embodiment of the present disclosure.
As shown in fig. 7, the computer system 700 includes a processing unit 701 that can execute various processes in the above-described embodiments according to a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data necessary for the operation of the system 700 are also stored. The processing unit 701, the ROM 702, and the RAM 703 are connected to each other by a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary. The processing unit 701 may be implemented as a CPU, a GPU, a TPU, an FPGA, an NPU, or other processing units.
In particular, the above described methods may be implemented as computer software programs according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising computer instructions that, when executed by a processor, implement the method steps described above. In such an embodiment, the computer program product may be downloaded and installed from a network via the communication section 709, and/or installed from the removable medium 711.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units or modules described in the embodiments of the present disclosure may be implemented by software or by programmable hardware. The units or modules described may also be provided in a processor, and the names of the units or modules do not in some cases constitute a limitation of the units or modules themselves.
As another aspect, the present disclosure also provides a computer-readable storage medium, which may be a computer-readable storage medium included in the electronic device or the computer system in the above embodiments; or it may be a separate computer readable storage medium not incorporated into the device. The computer readable storage medium stores one or more programs for use by one or more processors in performing the methods described in the present disclosure.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is possible without departing from the inventive concept. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Claims (20)
1. A method of data processing, comprising:
acquiring object information of a specified object from specified data related to video data;
determining a video clip related to the specified object in the video data according to the object information of the specified object;
an output video is generated based on the video segments.
2. The method of claim 1, wherein the specified data comprises one or more of: text data, picture data, audio data, the object information of the specified object is obtained from specified data related to the video data, including:
extracting a name of a specified object from the text data; and/or
Extracting a name of a specified object from the audio data; and/or acquiring an image of a specified object from the picture data.
3. The method of claim 2, wherein the determining a video segment related to the specified object in the video data according to the object information of the specified object comprises:
carrying out object identification on the video data to obtain a first video segment matched with the name of the specified object; and/or
And matching the image of the specified object with the video data to obtain a second video clip matched with the image of the specified object.
4. The method of claim 3, wherein the matching the image of the specified object with the video data resulting in a second video segment matching the image of the specified object comprises:
performing main body detection on the image of the specified object to obtain a first detection result;
performing main body detection on the video data to obtain a second detection result;
and obtaining the second video clip according to the similarity between the first detection result and the second detection result.
5. The method of claim 4, wherein the calculating a similarity between the first detection result and the second detection result comprises:
determining the first similarity based on a cosine distance between image features of the first detection result and the second detection result; and/or
Calculating a first differential hash code of the first detection result and a second differential hash code of the second detection result, and determining the second similarity based on a hamming distance between the first differential code and the second differential code.
6. The method of claim 4, wherein the generating an output video based on the video clip comprises:
obtaining the confidence of each video clip in the first video clip and the second video clip;
and generating the output video based on the video clips with the confidence degrees meeting the preset conditions.
7. The method of claim 3, wherein said generating an output video based on said video segments comprises any one of:
generating the output video based on the first video segment;
generating the output video based on the second video segment;
generating the output video based on the first video segment and the second video segment;
generating the output video based on an intersection of the first video segment and the second video segment.
8. The method of claim 1, wherein:
the designated object comprises a sight spot, the text data is a text in sight spot tourist notes or sight spot introduction, and the picture data is an image in sight spot tourist notes or sight spot introduction; or
The specified object comprises a product, the text data is text in a product description page, and the picture data is an image in the product description page.
9. The method of claim 1, further comprising:
acquiring the specified data according to any one or more of the following video data: title, abstract, keywords.
10. The method of claim 1, further comprising:
and synthesizing the part related to the output video in the specified data and the output video.
11. A method of data processing, comprising:
transmitting video data containing a specified object;
receiving output video information for obtaining an output video automatically generated based on a video clip of the video data.
12. The method according to claim 11, wherein the video clip is a video clip related to a specified object derived from the video data according to object information of the specified object obtained from the specified data, the specified data including one or more of: text data, picture data, audio data.
13. The method of claim 12, wherein:
the designated object is a sight spot, the text data is a text in sight spot travel notes or sight spot introductions, and the picture data is an image in sight spot travel notes or sight spot introductions; or
The specified object is a product, the text data is text in a product description, and the picture data is an image in the product description.
14. A data processing apparatus comprising:
a first acquisition module configured to acquire object information of a specified object from specified data related to video data;
a determining module configured to determine a video segment related to the specified object in the video data according to the object information of the specified object;
a generation module configured to generate an output video based on the video segments.
15. The apparatus of claim 14, further comprising:
a second obtaining module configured to obtain the specified data according to any one or more of the following of the video data: title, abstract, keywords.
16. The apparatus of claim 14, further comprising:
a synthesizing module configured to synthesize the portion of the specified data related to the output video with the output video.
17. A data processing apparatus comprising:
a transmission module configured to transmit video data containing a specified object;
a receiving module configured to receive output video information for acquiring an output video automatically generated based on a video clip of the video data.
18. An electronic device comprising a memory and a processor; wherein the memory is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method steps of any of claims 1-13.
19. A readable storage medium having stored thereon computer instructions which, when executed by a processor, carry out the method steps of any of claims 1-13.
20. A computer program product comprising computer instructions which, when executed by a processor, carry out the method steps of any of claims 1 to 13.
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