Social-Aware Video Frame Selection System
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Solution Overview
Problem
Current methods for selecting images from video sequences lack integration with social network information, failing to effectively identify and prioritize frames based on user relationships and geographic locations.
Innovation Solution
A method that analyzes video frames to identify social network objects and users, using GPS and facial recognition to select frames based on user proximity and relationships, scoring frames based on social network object affinity and popularity, and storing selected frames with metadata for user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image selection methods are used, then the process is simple and fast, but the selected images lack relevance to user social network interactions and geographic locations
Solution Approach 1:
The patent merges multiple data sources including video frames, social network information, and geographic location data into a unified selection system. The system combines visual analysis of video frames with social graph data and GPS coordinates to automatically select relevant images, integrating previously separate processing domains into a cohesive image selection methodology.
Solution Approach 2:
The patent introduces an intermediary processing layer that analyzes both video frames and social network objects to determine relevance. This intermediary system processes multiple data types through a unified analysis framework, using social network affinity scores and geographic proximity as mediating factors to bridge the gap between raw video data and user-relevant selections.
2Productivity
If manual image selection is used, then user control is maintained, but time consumption increases and efficiency decreases
Solution Approach 1:
The system performs self-service by automatically analyzing video frames, identifying social network objects, and selecting relevant images without requiring manual user intervention. The automated selection process uses social network affinity calculations and geographic location data to independently determine which images should be selected, eliminating the need for users to manually review and choose from extensive video frames.
Solution Approach 2:
The patent implements preliminary action by pre-processing video frames to identify and score potential social network objects before final selection. The system performs preliminary analysis of visual content, social graph relationships, and geographic metadata in advance, creating pre-ranked lists of relevant images that are ready for automatic selection without requiring users to perform time-consuming manual filtering.
3Adaptability or versatility
If social network information is integrated into image selection, then user engagement increases, but system complexity and processing requirements increase
Solution Approach 1:
The patent applies universality by designing a multi-functional system that handles video frame analysis, social network object identification, geographic location processing, and image selection within a single integrated framework. The same processing architecture serves multiple purposes: analyzing visual content, querying social graph data, evaluating geographic proximity, and ranking images based on combined criteria, thereby reducing overall system complexity despite the expanded functionality.
4Measurement precision
If frames are selected based on social network object affinity, then relevance to user relationships improves, but measurement and identification difficulty increases
Solution Approach 1:
The patent uses an intermediary analysis layer that processes video frames and social network data through a unified evaluation framework. This intermediary system translates complex social relationship data into quantifiable affinity scores by analyzing visual content against social graph information and geographic metadata, creating a measurable metric that bridges the gap between raw data and detectable social relevance.
Data Source
AI summary
In one embodiment, a mobile device analyzes frames before and after a particular frame of a real-time video to identify one or more social network objects, and selects one or more frames before and after the particular frame based on social network information for further storage in the mobile device.