CN110188241B - Intelligent manufacturing system and manufacturing method for events - Google Patents
Intelligent manufacturing system and manufacturing method for events Download PDFInfo
- Publication number
- CN110188241B CN110188241B CN201910483236.8A CN201910483236A CN110188241B CN 110188241 B CN110188241 B CN 110188241B CN 201910483236 A CN201910483236 A CN 201910483236A CN 110188241 B CN110188241 B CN 110188241B
- Authority
- CN
- China
- Prior art keywords
- event
- data
- intelligent
- labels
- module
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/78—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/783—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
- G06F16/7837—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using objects detected or recognised in the video content
- G06F16/784—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using objects detected or recognised in the video content the detected or recognised objects being people
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/78—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/7867—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using information manually generated, e.g. tags, keywords, comments, title and artist information, manually generated time, location and usage information, user ratings
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
- G06V20/42—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items of sport video content
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/46—Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/161—Detection; Localisation; Normalisation
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
- H04N21/4662—Learning process for intelligent management, e.g. learning user preferences for recommending movies characterized by learning algorithms
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/47—End-user applications
- H04N21/475—End-user interface for inputting end-user data, e.g. personal identification number [PIN], preference data
- H04N21/4756—End-user interface for inputting end-user data, e.g. personal identification number [PIN], preference data for rating content, e.g. scoring a recommended movie
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- Library & Information Science (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Signal Processing (AREA)
- Human Computer Interaction (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an intelligent manufacturing system and a manufacturing method of an event, which relate to the technical field of sports event manufacturing, and comprise a data aggregation module for aggregating event data provided by event data providers and the Internet; the intelligent analysis module extracts event labels of event data by utilizing an intelligent algorithm based on the constructed knowledge graph, and performs statistical analysis based on the event labels and the knowledge graph to generate feature labels; the invention utilizes a plurality of intelligent technologies to identify, refine and analyze the event data, generates a competition progress time line and a characteristic label, enables editors to quickly know the competition progress, and greatly improves the efficiency of program production.
Description
Technical Field
The invention relates to the technical field of sports event manufacturing, in particular to an intelligent manufacturing system and an intelligent manufacturing method for events.
Background
At present, the event video is a video type with very high attention, has a huge number of watching groups, and related products have good application prospects, such as sports events, and high timeliness is soul of sports news, how to quickly find a wanted lens and trouble program production for a long time, and traditional event program production mainly depends on manual work, such as:
1. editing an event program generally includes the steps of: viewing the game, conceiving the story, searching for the highlight and editing the highlight online;
2. adopting manual script to describe and record the scene, wherein the common script is to record the information of each finished lens or segment, and the content comprises a lens number, a scene, shot content, a time code and the like;
however, the above two conventional event programming has the following problems:
1. editing and making the event programs by the first method, the game profile cannot be quickly known, the highlight is difficult to quickly locate, and a great deal of time is required to be spent in watching the game and searching for the highlight to acquire the information, so that the making efficiency is low;
2. the manual script is adopted to assist the program production, although the efficiency of the production of the event program is improved to a certain extent, the content of the manual script is limited, the combination degree with editing tools is not high, and the editing personnel can hardly intuitively know the competition profile from the script content;
and because the existing event making method is not efficient, event gathering display with some dimensions is often abandoned due to time urgency, and the game profile of the team and the player is difficult to display in multiple directions.
Disclosure of Invention
The invention aims at: in order to solve the problems that the existing event making method is low in efficiency and needs to consume a large amount of time, the invention provides an event intelligent making system and an event intelligent making method.
The invention adopts the following technical scheme for realizing the purposes:
an intelligent manufacturing system for an event comprises a data aggregation module, an intelligent analysis module and a selection editing module:
and a data aggregation module: for aggregating event data provided by event data providers and the internet;
and an intelligent analysis module: constructing a knowledge graph, extracting event labels of event data by using an intelligent algorithm based on the constructed knowledge graph, and carrying out statistical analysis based on the event labels and the knowledge graph to generate feature labels;
selecting and editing module: and displaying the result of the statistical analysis in the event making tool, classifying and segmenting to present event fragments, selecting a time fragment corresponding to the event fragment by an editor according to the result of the statistical analysis and the characteristic label, and dragging the time fragment to a time line to finish the event editing.
Furthermore, the event data provided by the event data provider is usually structured data, so that the event data can be directly used by the intelligent analysis module, but the event data provided by the internet is usually unstructured, so that the data aggregation module needs to utilize the NLP technology to carry out structuring processing on the event data provided by the internet according to a preset data aggregation rule, and the intelligent analysis module is convenient to extract event labels.
Further, the knowledge graph constructed by the intelligent analysis module includes, but is not limited to, event rules, event live rules, game information, athlete information, and game team information.
Further, the intelligent algorithms utilized by the intelligent analysis module include, but are not limited to, OCR recognition technology, time-series behavior recognition technology, transition recognition technology, LOGO recognition technology, and face recognition technology.
Further, the intelligent analysis module comprises a training data module, training data in the training data module comprises, but is not limited to, event video and event data, and the intelligent analysis module optimizes and improves the accuracy of event tag extraction by using the training data.
Further, the intelligent analysis module performs statistical analysis based on event tags and knowledge maps, including but not limited to event distribution analysis of classification segmentation, bifurcation trend line analysis of event trend, and single-node score K-ray diagram analysis.
Further, the intelligent analysis module performs visual saliency scoring for each event, and the visual saliency scoring rule is formulated based on the knowledge graph and the event common knowledge.
An intelligent manufacturing method for an event comprises the following steps:
s1, acquiring event data:
the data aggregation module acquires event data from event data providers and the Internet;
s2, extracting event labels and generating a competition progress time line:
the intelligent analysis module extracts events in event data and event attribute data to mark a competition progress time line, so as to obtain event labels;
s3, analyzing the event label to generate a characteristic label:
the intelligent analysis module performs statistical analysis based on the event labels and the knowledge graph to generate feature labels;
s4, selecting and editing:
the statistical analysis result is displayed in an event making tool, and an editor selects event fragments by utilizing a selection and editing module according to the statistical analysis result and the characteristic label, and drags the time fragments corresponding to the event fragments to a time line to complete the editing of the event.
Further, the event data acquired in S1 includes event video data and event data.
Further, the step S2 specifically includes the following steps:
s2.1: extracting events and event attribute data in an event data protocol of an event data provider to mark a event process time line, and obtaining event labels;
s2.2: and performing video and audio analysis on the event video data, extracting event and event attribute data, and marking a event progress time line to obtain an event tag.
Further, the S2.2 specifically is:
s2.2.1: removing the non-event pictures;
s2.2.2: dividing the long shot segment by using a transition identification technology;
s2.2.3: and performing behavior recognition under a multi-person scene and positioning an event key frame interval by utilizing a face recognition technology, an OCR recognition technology, a time sequence behavior recognition technology and the like.
Further, the generating the feature tag in S3 includes, but is not limited to, the following:
A. counting to obtain a trend chart of a plurality of indexes of the team;
B. counting to obtain a player index trend chart;
C. counting to obtain the distribution condition of different types of events in the competition process;
D. counting to obtain a bifurcation trend line of the event trend;
E. and displaying the trend of the competition by adopting a K line graph mode.
The beneficial effects of the invention are as follows:
1. the invention can identify, refine and analyze the competition data by utilizing various intelligent technologies, generate a competition progress time line and competition feature labels, provide multi-azimuth data analysis result display, enable editors to liberate from competition videos of more than 100 minutes, quickly know the competition progress, put more energy on more specialized story conception and program production, and greatly improve the efficiency of program production.
2. According to the invention, the intelligent analysis result is directly displayed in the editing tool, fragments can be directly selected, dragged and online edited, the linkage between the intelligent analysis result and the editing tool is enhanced, the intelligent analysis result and the editing tool are highly flexible, editors can display content and modes in a self-defined manner, resources for program production are enriched, and editors can rapidly produce multi-dimensional highlight content.
3. According to the invention, the event visual highlight scoring rule is added, so that highlight event fragments with higher scores can be automatically recommended to editors, the editors can finish the production of event highlights, and the quick release of Internet news is facilitated.
Drawings
FIG. 1 is a schematic diagram of an intelligent manufacturing system for an event according to the present invention.
FIG. 2 is a flow chart of the intelligent manufacturing method of the event of the present invention.
FIG. 3 is an exemplary diagram of the present invention generating event tags.
FIG. 4 is an exemplary diagram of a pick editing step of the present invention.
Detailed Description
For a better understanding of the present invention, reference is made to the following description of the invention, taken in conjunction with the accompanying drawings and the following examples.
Example 1
As shown in fig. 1, this embodiment provides an intelligent manufacturing system for an event, including a data aggregation module, an intelligent analysis module, and a selection editing module:
and a data aggregation module: for aggregating event data provided by event data providers and the internet;
and an intelligent analysis module: constructing a knowledge graph, extracting event labels of event data by using an intelligent algorithm based on the constructed knowledge graph, and carrying out statistical analysis based on the event labels and the knowledge graph to generate feature labels;
selecting and editing module: and displaying the result of the statistical analysis in the event making tool, classifying and segmenting to present event fragments, selecting a time fragment corresponding to the event fragment by an editor according to the result of the statistical analysis and the characteristic label, and dragging the time fragment to a time line to finish the event editing.
Because the event data provided by the event data provider is usually structured data, the event data can be directly used by the intelligent analysis module, but the event data provided by the internet is usually unstructured, so that the data aggregation module needs to perform structured processing on the event data provided by the internet by utilizing an NLP technology according to a preset data aggregation rule, the intelligent analysis module is convenient to extract event labels, the data aggregation rule in the embodiment is to extract content such as behavior actions, subjects, objects, world time, game time, attack countdown, scores and the like by using NLP analysis, and the content is converted and recorded into the event structure data.
The knowledge graph constructed by the intelligent analysis module comprises, but is not limited to, event rules, event live broadcast rules, game information, athlete information and game team information; intelligent algorithms include, but are not limited to, OCR recognition technology, timing behavior recognition technology, transition recognition technology, LOGO recognition technology, and face recognition technology.
And the intelligent analysis module comprises a training data module, training data in the training data module comprises but is not limited to event video and event data, and the intelligent analysis module optimizes and improves the accuracy of event tag extraction by using the training data.
The intelligent analysis module performs statistical analysis based on event labels and knowledge maps, including but not limited to event distribution analysis of classification and segmentation, bifurcation trend line analysis of event trend and single-section score K line analysis, is convenient for showing to a selection editing page for viewing and use, and is convenient for editors to rapidly master competition event points and competition trend and rapidly think about stories for editing.
As shown in fig. 2, this embodiment also provides an intelligent manufacturing method for an event, including the following steps:
s1, acquiring event data:
the data aggregation module acquires event data from event data providers and the Internet, wherein the event data comprises event video data and event data;
s2, as shown in FIG. 3, extracting event labels to generate a competition progress time line:
the intelligent analysis module extracts events in event data and event attribute data to mark a competition progress time line to obtain event labels, wherein the event labels are specifically as follows:
s2.1: extracting event and event attribute data in event data protocols of event data providers to mark a game progress time line to obtain event labels, wherein in the embodiment, basketball events are taken as examples, and the data protocols comprise various events and event attributes such as shooting, catching a basket, fouls, pauses and the like;
s2.2: performing video and audio analysis on the event video data, extracting event and event attribute data, marking a event process time line, and obtaining an event tag, wherein the extracted events include player actions in the video and image-text package template events, wherein the image-text package template events comprise score, event, person changing and the like; the S2.2 is specifically as follows:
s2.2.1: removing non-event pictures, such as advertisements, cheering teams, pauses, highlight playback and the like, specifically:
a. extracting a packing template of the competition video;
b. extracting different element areas of a main team, a guest team, scores, time and the like;
c. preprocessing the image by dividing elements;
d. identifying non-event pictures according to event rules, live broadcast rules and the like;
s2.2.2: dividing the long shot segment by using a transition identification technology;
s2.2.3: performing behavior recognition under a multi-person scene and positioning event key frame intervals by utilizing a face recognition technology, an OCR recognition technology, a time sequence behavior recognition technology and the like, such as shooting, basket feeding, basket buckling, capping, robbing and the like;
s3, analyzing the event label to generate a characteristic label:
the intelligent analysis module performs statistical analysis based on the event labels and the knowledge graph to generate feature labels; generating feature tags in S3 includes, but is not limited to, the following:
A. counting to obtain a team multi-index trend graph, such as score, hit rate and the like;
B. counting to obtain a player index trend graph, such as score, hit rate and the like;
C. counting to obtain the permission of different types of events in the competition process;
D. counting to obtain a bifurcation trend line of the event trend;
E. displaying the trend of the competition by adopting a K line graph mode;
as shown in fig. 4, the statistical analysis results displayed in the event making tool are shown, wherein the horizontal axis is a time axis, the vertical axis is an event type, the dotted line represents a time interval of a game bar, the bar graph represents a K line graph of the dominant change of a host team, the curve represents a game trend line, and the small dots represent event segments corresponding to the horizontal axis and the vertical axis; editors can customize the dimension and content of statistical analysis results, such as whether trend lines are displayed, whether the vertical axes display the dimensions of penalty hits, shooting hits, fouls, wonderful playback, XX player shooting hit rate, and the like;
s4, selecting and editing:
as shown in fig. 4, the statistical analysis result is displayed in the event making tool, the editor selects event fragments according to the statistical analysis result and the feature tag by using the selection and editing module, and drags the time fragments corresponding to the event fragments to the time line to complete the event editing.
The above description is only a preferred embodiment of the present invention, and the patent protection scope of the present invention is defined by the claims, and all equivalent structural changes made by the specification and the drawings of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. The intelligent manufacturing system for the events is characterized by comprising a data aggregation module, an intelligent analysis module and a selection editing module:
and a data aggregation module: for aggregating event data provided by event data providers and the internet;
and an intelligent analysis module: constructing a knowledge graph, extracting event labels of event data by using an intelligent algorithm based on the constructed knowledge graph, and carrying out statistical analysis based on the event labels and the knowledge graph to generate feature labels;
selecting and editing module: displaying the result of statistical analysis in an event making tool, classifying and sectionally presenting event fragments, selecting a time fragment corresponding to the event fragment by an editor according to the result of statistical analysis and a characteristic label, and dragging the time fragment to a time line to finish event editing;
the event data provided by the event data provider is usually structured data, the data aggregation module utilizes an NLP technology to carry out structured processing on the event data provided by the Internet according to a preset data aggregation rule, and the intelligent analysis module is convenient to extract event labels;
the intelligent analysis module extracts events in event data and event attribute data to mark a competition progress time line to obtain event labels, and the specific steps are as follows:
s2.1: extracting events and event attribute data in an event data protocol of an event data provider to mark a event process time line, and obtaining event labels;
s2.2: video and audio analysis is carried out on the event video data, event and event attribute data are extracted to mark a event progress time line, and an event label is obtained, specifically:
s2.2.1: removing the non-event pictures;
s2.2.2: dividing the long shot segment by using a transition identification technology;
s2.2.3: performing behavior recognition under a multi-person scene and positioning an event key frame interval by utilizing a face recognition technology, an OCR recognition technology and a time sequence behavior recognition technology;
step S2.2.1, when removing the non-event frame, is specifically:
a. extracting a packing template of the competition video;
b. extracting different element areas of a main team, a guest team, scores and time;
c. preprocessing the image by dividing elements;
d. and identifying non-event pictures according to the event rules and the live broadcast rules.
2. The system of claim 1, wherein the knowledge graph constructed by the intelligent analysis module includes, but is not limited to, event rules, event live rules, game information, athlete information, and team information.
3. An event intelligence production system according to claim 1, wherein the intelligence algorithms utilized by the intelligence analysis module include, but are not limited to, OCR recognition technology, time series behavior recognition technology, transition recognition technology, LOGO recognition technology and face recognition technology.
4. An event intelligence production system according to claim 1 wherein the intelligence analysis module comprises a training data module, the training data within the training data module including but not limited to event video and event data, the intelligence analysis module using the training data to optimize and enhance the accuracy of event tag extraction.
5. An event intelligence production system according to claim 1 wherein the intelligence analysis module performs statistical analysis based on event tags and knowledge maps including but not limited to event distribution analysis of classification segments, bifurcation trend line analysis of event trends and single section K-wire diagram analysis.
6. The intelligent manufacturing method for the event is characterized by comprising the following steps:
s1, acquiring event data:
the data aggregation module acquires event data from event data providers and the Internet;
s2, extracting event labels and generating a competition progress time line:
the intelligent analysis module extracts events in event data and event attribute data to mark a competition progress time line, so as to obtain event labels;
s3, analyzing the event label to generate a characteristic label:
the intelligent analysis module performs statistical analysis based on the event labels and the knowledge graph to generate feature labels;
s4, selecting and editing:
the statistical analysis result is displayed in an event making tool, an editor selects event fragments by utilizing a selection and editing module according to the statistical analysis result and the characteristic label, and drags the time fragments corresponding to the event fragments to a time line to complete the editing of the event;
the event data provided by the event data provider is usually structured data, the data aggregation module utilizes an NLP technology to carry out structured processing on the event data provided by the Internet according to a preset data aggregation rule, and the intelligent analysis module is convenient to extract event labels;
the step S2 specifically comprises the following steps:
s2.1: extracting events and event attribute data in an event data protocol of an event data provider to mark a event process time line, and obtaining event labels;
s2.2: video and audio analysis is carried out on the event video data, event and event attribute data are extracted to mark a event progress time line, and an event label is obtained, specifically:
s2.2.1: removing the non-event pictures;
s2.2.2: dividing the long shot segment by using a transition identification technology;
s2.2.3: performing behavior recognition under a multi-person scene and positioning an event key frame interval by utilizing a face recognition technology, an OCR recognition technology and a time sequence behavior recognition technology;
step S2.2.1, when removing the non-event frame, is specifically:
a. extracting a packing template of the competition video;
b. extracting different element areas of a main team, a guest team, scores and time;
c. preprocessing the image by dividing elements;
d. and identifying non-event pictures according to the event rules and the live broadcast rules.
7. The intelligent event making method according to claim 6, wherein the event data acquired in S1 includes event video data and event data.
8. The method for intelligent production of an event according to claim 6, wherein the generating of the feature tag in S3 includes, but is not limited to, the following:
A. counting to obtain a trend chart of a plurality of indexes of the team;
B. counting to obtain a player index trend chart;
C. counting to obtain the distribution condition of different types of events in the competition process;
D. counting to obtain a bifurcation trend line of the event trend;
E. and displaying the trend of the competition by adopting a K line graph mode.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910483236.8A CN110188241B (en) | 2019-06-04 | 2019-06-04 | Intelligent manufacturing system and manufacturing method for events |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910483236.8A CN110188241B (en) | 2019-06-04 | 2019-06-04 | Intelligent manufacturing system and manufacturing method for events |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110188241A CN110188241A (en) | 2019-08-30 |
| CN110188241B true CN110188241B (en) | 2023-07-25 |
Family
ID=67720357
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910483236.8A Active CN110188241B (en) | 2019-06-04 | 2019-06-04 | Intelligent manufacturing system and manufacturing method for events |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110188241B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111177396B (en) * | 2019-11-13 | 2023-06-06 | 浙江广播电视集团 | Automatic analysis and visualization method for basketball event shooting event combined with knowledge graph |
| CN111770359B (en) * | 2020-06-03 | 2022-10-11 | 苏宁云计算有限公司 | Event video clipping method, system and computer readable storage medium |
| CN112291574B (en) * | 2020-09-17 | 2023-07-04 | 上海东方传媒技术有限公司 | Large-scale sports event content management system based on artificial intelligence technology |
| CN112633087A (en) * | 2020-12-09 | 2021-04-09 | 新奥特（北京）视频技术有限公司 | Automatic journaling method and device based on picture analysis for IBC system |
| CN113423000B (en) * | 2021-06-11 | 2024-01-09 | 完美世界征奇(上海)多媒体科技有限公司 | Video generation method and device, storage medium and electronic device |
| CN117132925B (en) * | 2023-10-26 | 2024-02-06 | 成都索贝数码科技股份有限公司 | Intelligent stadium method and device for sports event |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101296322A (en) * | 2007-04-27 | 2008-10-29 | 新奥特硅谷视频技术有限责任公司 | Sports event logging system |
| CN104363478A (en) * | 2014-11-28 | 2015-02-18 | 中国传媒大学 | Program label association rule analyzing system and method |
| CN105144741A (en) * | 2013-03-05 | 2015-12-09 | 英国电讯有限公司 | Video data provision |
| CN105704503A (en) * | 2016-01-21 | 2016-06-22 | 成都索贝数码科技股份有限公司 | Application method for online integrated pipe control application cloud platform based on saas media cloud technology |
| CN105893499A (en) * | 2016-03-30 | 2016-08-24 | 乐视控股（北京）有限公司 | Athletics competition data displaying method and device |
| CN107992464A (en) * | 2017-12-08 | 2018-05-04 | 浙江大学 | The method for visualizing of single game Basketball Match data |
| CN108132966A (en) * | 2017-11-28 | 2018-06-08 | 北京洪泰同创信息技术有限公司 | Knowledge mapping generation method and device |
| CN109121021A (en) * | 2018-09-28 | 2019-01-01 | 北京周同科技有限公司 | A kind of generation method of Video Roundup, device, electronic equipment and storage medium |
| CN109635171A (en) * | 2018-12-13 | 2019-04-16 | 成都索贝数码科技股份有限公司 | A kind of fusion reasoning system and method for news program intelligent label |
| CN109710806A (en) * | 2018-12-06 | 2019-05-03 | 苏宁体育文化传媒(北京)有限公司 | The method for visualizing and system of football match data |
-
2019
- 2019-06-04 CN CN201910483236.8A patent/CN110188241B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101296322A (en) * | 2007-04-27 | 2008-10-29 | 新奥特硅谷视频技术有限责任公司 | Sports event logging system |
| CN105144741A (en) * | 2013-03-05 | 2015-12-09 | 英国电讯有限公司 | Video data provision |
| CN104363478A (en) * | 2014-11-28 | 2015-02-18 | 中国传媒大学 | Program label association rule analyzing system and method |
| CN105704503A (en) * | 2016-01-21 | 2016-06-22 | 成都索贝数码科技股份有限公司 | Application method for online integrated pipe control application cloud platform based on saas media cloud technology |
| CN105893499A (en) * | 2016-03-30 | 2016-08-24 | 乐视控股（北京）有限公司 | Athletics competition data displaying method and device |
| CN108132966A (en) * | 2017-11-28 | 2018-06-08 | 北京洪泰同创信息技术有限公司 | Knowledge mapping generation method and device |
| CN107992464A (en) * | 2017-12-08 | 2018-05-04 | 浙江大学 | The method for visualizing of single game Basketball Match data |
| CN109121021A (en) * | 2018-09-28 | 2019-01-01 | 北京周同科技有限公司 | A kind of generation method of Video Roundup, device, electronic equipment and storage medium |
| CN109710806A (en) * | 2018-12-06 | 2019-05-03 | 苏宁体育文化传媒(北京)有限公司 | The method for visualizing and system of football match data |
| CN109635171A (en) * | 2018-12-13 | 2019-04-16 | 成都索贝数码科技股份有限公司 | A kind of fusion reasoning system and method for news program intelligent label |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110188241A (en) | 2019-08-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110188241B (en) | Intelligent manufacturing system and manufacturing method for events | |
| Xiong et al. | A pursuit of temporal accuracy in general activity detection | |
| CN110245259B (en) | Video labeling method and device based on knowledge graph and computer readable medium | |
| Merler et al. | Automatic curation of sports highlights using multimodal excitement features | |
| US20220270369A1 (en) | Intelligent cataloging method for all-media news based on multi-modal information fusion understanding | |
| CN102833638B (en) | Automatic video segmentation and annotation method and system based on caption information | |
| US20170228600A1 (en) | Analysis of video game videos for information extraction, content labeling, smart video editing/creation and highlights generation | |
| EP2560379A1 (en) | Content search device, method, and program | |
| Wang et al. | Improving human action recognition by non-action classification | |
| CN113779381B (en) | Resource recommendation method, device, electronic equipment and storage medium | |
| Zhao et al. | A novel system for visual navigation of educational videos using multimodal cues | |
| Bhalla et al. | A multimodal approach for automatic cricket video summarization | |
| Merler et al. | Automatic curation of golf highlights using multimodal excitement features | |
| CN110765314A (en) | Video semantic structural extraction and labeling method | |
| CN102004795B (en) | Hand language searching method | |
| CN114691923A (en) | System and method for computer learning | |
| Prabhakar et al. | Categorizing turn-taking interactions | |
| CN110188237B (en) | Data aggregation system and method for intelligent manufacturing of events | |
| CN107609104B (en) | Method and system for searching related video according to video image material | |
| Faulkner et al. | Tenniset: a dataset for dense fine-grained event recognition, localisation and description | |
| Ritter et al. | An extensible tool for the annotation of videos using segmentation and tracking | |
| Elgamml et al. | Semantic analysis in soccer videos using support vector machine | |
| CN105975054A (en) | Method and device for information processing | |
| CN110008374B (en) | It is a kind of to select edit methods for what race intelligently made | |
| CN110381367B (en) | Video processing method, video processing equipment and computer readable storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |