Commercial Ad Detection via Profile and Audio Transitions
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Solution Overview
Problem
Advertisers face challenges in confirming the occurrence and placement of commercial advertisements across various media platforms, including television and internet streaming, due to the lack of efficient detection methods.
Innovation Solution
The use of profile change detection and random audio detection techniques to identify transitions between television program segments and commercial advertisements in digital television feeds, allowing for the generation of commercial transition hints and confirmation of advertisement placements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional advertisement confirmation methods are used, then advertisers can obtain basic advertisement placement information, but the accuracy and reliability of detection is insufficient
Solution Approach 1:
The detection system is divided into multiple independent modules: profile change detection module, random audio detection module, and commercial transition hint generation module. Each module performs a specific detection function, and their results are combined to improve overall detection accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent introduces 'commercial transition hints' as an intermediary element that bridges the gap between raw detection data and confirmed advertisement placements. These hints serve as intermediate evidence that requires corroboration from multiple detection methods before confirming an advertisement occurrence, thereby improving reliability without requiring direct complex analysis of all raw data.
2Reliability
If multiple detection methods are employed to improve advertisement confirmation reliability, then detection accuracy increases, but the complexity of the detection system increases
Solution Approach 1:
The system segments reliability verification into distinct detection pathways: visual profile change detection, audio randomness detection, and transition hint analysis. Each pathway independently contributes to reliability, and their modular structure allows the system to leverage multiple methods without creating monolithic complexity.
Solution Approach 2:
The detection system is designed with multi-functional capabilities where the same modular architecture handles both profile change detection and random audio detection. This universal structure allows the system to employ multiple detection methods while avoiding the complexity overhead of separate dedicated systems for each method.
3Measurement precision
If profile change detection and random audio detection are used to identify commercial transitions, then advertisement transition identification accuracy improves, but processing requirements increase
Solution Approach 1:
The system applies partial action by focusing detection efforts on transition points rather than continuously analyzing entire advertisement segments. Profile change detection and random audio detection are specifically targeted at identifying transitions, which are the critical moments for confirmation, rather than uniformly processing all video and audio data throughout the advertisement duration.
Solution Approach 2:
The system performs preliminary detection of profile changes and audio randomness patterns before final advertisement confirmation. By pre-identifying potential transition points using these detection methods, the system reduces the need for more computationally intensive analysis of entire advertisement segments, thereby improving accuracy while managing processing resources.
Data Source
AI summary
Methods and apparatus to detect commercial advertisements associated with media presentations are disclosed. An example method involves receiving a video frame and detecting a change in box-formatting between the video frame and a subsequent video frame. A transition between the video frame and the subsequent video frame is indicated as a commercial advertisement transition based on the detected change in box-formatting.