Digest Video Scene Selection Using SNS Interest Signals
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Solution Overview
Problem
Existing systems struggle to accurately determine a viewer's interest or concern from social networking service data, leading to inadequate generation of relevant video content.
Innovation Solution
An information processing apparatus that utilizes a specification unit to analyze scene-related information, including keywords and metadata, to generate a digest video by selecting and combining clip videos based on viewer interest, using imaging devices and social networking data to identify key scenes and events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If video content is generated based on general SNS information, then content creation is simplified, but the ability to reflect specific viewer interest is insufficient
Solution Approach 1:
The system segments SNS information into specific scene-related data (keywords, hashtags, posts) and processes it through multiple analysis units (specification unit, extraction unit, determination unit) to precisely identify viewer interests in specific scenes, resolving the contradiction between ease of operation and measurement precision
2Measurement precision
If scene-related information is extracted from SNS, then viewer interest can be identified, but the complexity of information processing increases
Solution Approach 1:
The information processing system is divided into functional units: specification unit for defining scene-related information, extraction unit for obtaining data from SNS, and determination unit for selecting clip videos. This segmentation reduces overall system complexity while maintaining high measurement precision for scene interest identification
Solution Approach 2:
The specification unit acts as an intermediary that defines and standardizes scene-related information formats before processing. This intermediary layer simplifies the interaction between the extraction unit and determination unit, reducing processing complexity while maintaining accurate scene interest identification
3Reliability
If all captured video is processed, then complete scene coverage is achieved, but processing efficiency decreases
Solution Approach 1:
The extraction unit selectively extracts only the necessary scene-related information from SNS posts and the determination unit extracts relevant clip videos based on extracted information. This extraction approach maintains complete scene coverage reliability while significantly improving processing efficiency by avoiding unnecessary analysis of unrelated video content
4Measurement precision
If auxiliary information is used for video selection, then viewer interest reflection improves, but the difficulty of determining appropriate scenes increases
Solution Approach 1:
The system uses auxiliary information from SNS (keywords, hashtags, post content) as feedback to guide the determination unit in selecting appropriate clip videos. This feedback mechanism improves viewer interest reflection while reducing selection difficulty by providing clear criteria for scene identification and filtering
Data Source
AI summary
Provided is an information processing apparatus including a specification unit that specifies auxiliary information for generating a digest video on the basis of scene-related information regarding a scene occurring in an event.