Video Demarcation Detection Using Logo Templates
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Solution Overview
Problem
Existing video content processing systems fail to accurately identify and segment video demarcations, such as transitions from live to non-live content, without prior knowledge of marker content, leading to inefficiencies in content type detection and user experience.
Innovation Solution
A system and method that analyze logos and marker frames in video programs to determine video demarcations, using logo templates and marker frame processors to identify transitions and generate processed video segments, allowing for the detection of video demarcations without prior knowledge of marker content, enabling efficient segmentation of content types like highlights from live events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video content processing systems use prior knowledge of marker content to identify video demarcations, then the identification accuracy is improved, but the system complexity and requirement for pre-programmed knowledge increases
Solution Approach 1:
The system performs self-service by automatically learning marker content characteristics from the video stream itself without requiring external pre-programmed knowledge. The logo template processor extracts and stores logo templates directly from the video content, enabling the system to identify video demarcations autonomously based on learned patterns rather than predefined markers.
Solution Approach 2:
The system creates copies of logo templates from the video content and stores them in a database for repeated use. Instead of requiring prior knowledge of marker content, the system copies and stores representative logo images from the video stream, then uses these copied templates to identify video demarcations through comparison and matching processes.
2Measurement precision
If the system analyzes every frame to identify logos and marker frames, then the detection accuracy is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by extracting and storing logo templates from the video content before the actual video demarcation identification process. The logo template processor pre-processes the video to create a library of logo templates that can be quickly referenced during marker frame identification, avoiding the need to analyze every frame from scratch.
Solution Approach 2:
The system segments the video processing into distinct functional modules: logo template processing, marker frame processing, and video demarcation identification. This segmentation allows each module to specialize in specific tasks, with the logo template processor extracting templates once and the marker frame processor using these templates efficiently to identify demarcations without re-processing the entire video content.
3Adaptability or versatility
If the system stores multiple logo templates in a database, then the versatility of video content recognition is improved, but the storage requirements and database complexity increase
Solution Approach 1:
The system achieves universality by creating a multi-functional logo template database that serves multiple purposes: identifying different types of video demarcations, recognizing various logo types (station logos, score panels, etc.), and adapting to different video content formats. A single database structure handles diverse video content recognition tasks without requiring separate storage systems for each content type.
Data Source
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AI summary
Particular embodiments analyze logos found in a video program to determine video demarcations in the video program. For example, a video demarcation may be content that marks ("marker content") a transition from a first video content type to a second video content type. Marker content may be used so the user knows that a transition is occurring. Particular embodiments analyze the logos found in a video program to determine the video demarcations in the video. The video is first analyzed to determine logos in the video program. Once these logos are determined, particular embodiments may re-analyze the video program to identify marker frames that include the marker content that signal the transitions to a different video content types. The marker frames may be determined without any prior knowledge of the marker content. Then, particular embodiments may use the marker frames to determine video segments.