Video Seek System Session Information Extraction
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Solution Overview
Problem
Conventional thumbnail-based seek functions in video playback devices are inefficient for certain types of video content, such as sporting events, where similar scenes result in uninformative thumbnails, making it difficult for users to find specific locations.
Innovation Solution
A seek content extraction system that generates and displays session information alongside thumbnails, using machine learning techniques like R-CNN and SVM to identify and extract relevant information from video frames, such as game information cards or speaker topics, to provide users with visual feedback during seeking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional thumbnails are displayed for video content, then the seek function is provided, but the thumbnails become uninformative when scenes do not change much
Solution Approach 1:
The patent segments the video content into multiple frames and extracts session information from each frame individually. By processing frames at different time points and extracting relevant information (such as speaker names, topics, or key visual elements) from each segment, the system creates a comprehensive set of thumbnails that collectively provide sufficient information for effective seeking, even when individual frames appear similar.
Solution Approach 2:
The patent adds a temporal dimension to the thumbnail display by organizing thumbnails according to their corresponding time positions in the video. It also adds an informational dimension by superimposing or adjacent-displaying extracted session information (text labels describing content) alongside the visual thumbnails. This multi-dimensional approach transforms static, potentially identical-looking images into a structured, information-rich interface that enables precise location identification.
2Measurement precision
If manual tagging of information is used, then accurate session information is obtained, but computational resources are excessively consumed
Solution Approach 1:
The system employs automated computer vision and natural language processing algorithms that independently analyze video frames and generate session information without human intervention. The machine learning models automatically detect objects, recognize text, identify speakers, and summarize content, enabling the system to serve itself by generating accurate metadata autonomously from the video content itself.
Solution Approach 2:
The patent replaces manual tagging processes with automated computational systems. Instead of requiring human annotators to review and label each frame, the system uses computer vision algorithms to detect and extract information automatically. This substitution of mechanical manual labor with automated computational mechanisms significantly reduces computational resource expenditure while maintaining or improving accuracy through advanced machine learning techniques.
Data Source
AI summary
A seek content extraction system analyzes frames of video content and identifies locations in the frames where session information is displayed. This session information refers to information that is displayed as part of video content and that describes, for a particular location in the video content, what is currently happening in the video content at that particular location. This session information is extracted from each of multiple frames, and for a given frame the extracted session information is associated with the frame. While the user is seeking forward or backward through the video content, a thumbnail of the frame at a given location in the video content is displayed along with the extracted session information associated with the frame.