Automated Content Recognition Database for Real-Time TV Ad Detection
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Solution Overview
Problem
Current automated systems for detecting and identifying TV commercials in real-time across multiple channels and markets are inefficient and costly, as they rely on manual processes and struggle to scale to handle hundreds of video streams while maintaining computational and storage costs within reasonable limits.
Innovation Solution
An automated content recognition database method that ingests audiovisual streams, determines candidate segments by analyzing frames for matches in known advertisements, and removes identified commercial segments from the primary content database, using a path pursuit algorithm to refine matches and reduce false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated content recognition is used to detect commercials in real-time across multiple channels, then detection accuracy and automation level improve, but computational cost and storage requirements increase
Solution Approach 1:
The system segments video content by creating separate databases for program content and advertisement content. The content recognition database is divided into distinct data structures (program content database and advertisement database) that are maintained separately. This segmentation allows the system to process and match content more efficiently without requiring all computational resources to handle the entire video stream simultaneously, thus reducing overall computational cost while maintaining high automation levels.
Solution Approach 2:
The system performs preliminary action by pre-processing and storing advertisement content in a dedicated advertisement database before actual commercial detection occurs. Advertisements are ingested, processed, and organized in advance with their metadata and characteristics stored for rapid matching. This preliminary preparation significantly reduces the computational burden during real-time detection, as the system only needs to match against pre-organized advertisement data rather than analyzing all content from scratch.
2Productivity
If real-time monitoring of hundreds of video streams is implemented, then commercial detection coverage improves, but system complexity and processing load increase
Solution Approach 1:
The system handles multiple video streams by segmenting the processing task into separate database operations. Each stream's content is matched against the pre-existing program content database and advertisement database independently. This segmentation allows parallel processing of multiple streams without requiring complex inter-stream coordination, thereby managing system complexity while maintaining high detection coverage across hundreds of channels.
Solution Approach 2:
The system uses copying by maintaining duplicate databases (program content database and advertisement database) that can be independently queried. When monitoring multiple streams, the system copies relevant content characteristics from incoming streams and matches them against the stored databases rather than processing entire video streams in real-time. This approach reduces processing load while maintaining comprehensive detection coverage across numerous channels.
3Measurement precision
If candidate segments are tested against known advertisements in a comprehensive database, then match accuracy improves, but processing time increases
Solution Approach 1:
The system applies preliminary action by pre-organizing advertisement content in a structured database with metadata, tags, and hierarchical categorization before matching occurs. Advertisements are processed in advance to extract key characteristics and store them in optimized data structures. This preliminary organization enables rapid retrieval and comparison during candidate segment testing, maintaining high match accuracy while significantly reducing the time required to search through the advertisement database.
Solution Approach 2:
The matching process is segmented into multiple stages: first testing candidate segments against the program content database, then against the advertisement database only if needed. The system segments the search space and applies different matching thresholds at different stages. This segmented approach ensures high match accuracy through thorough testing while reducing overall processing time by eliminating unnecessary full-database searches for obvious non-matches.
Data Source
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AI summary
A system and method for the automated real-time detection and processing of commercial messages, public service announcements or similar short-duration, repeated TV programing segments occurring in one or more broadcast video steams is described. A process is utilized that identifies possible commercial segments by identifying discrete video segments that have specific short durations among other attributes. Video segments that appear to have these characteristics are considered likely to be television commercials, promotions, or public service announcements and are stored in a TV Ad database which is separate from the primary television content database. Incoming video from a plurality of television programming sources is process into fingerprints and placed in a master TV content database. At the same time, said incoming programming is tested for matches in a TV ad database. If a match is found, then the presumed TV ad is removed from the master content database.