Ad Break Detection Using Audio-Video Scene Break Scoring
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Solution Overview
Problem
The overwhelming abundance of multimedia content and dynamic advertising options has led to a need for automating and streamlining processes to reduce human analysis and improve contextual understanding in media content, particularly in on-demand media service providers and advertising exchanges.
Innovation Solution
A system and method for ad break detection in media items using audio and video analysis, combined with computer vision and machine learning, to identify candidate ad break timestamps and select optimal ad insertion points based on scene break scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human personnel are employed to perform manual analysis and review of media content, then contextual understanding and quality control are improved, but labor costs and processing time increase
Solution Approach 1:
The patent replaces manual human analysis with an automated computer vision system that uses machine learning models to detect scene breaks and generate ad break timestamps. The system processes video frames, audio tracks, and metadata automatically without human intervention, substituting mechanical human cognitive processes with automated computational algorithms.
Solution Approach 2:
The system enables self-service by allowing the media processing pipeline to automatically generate ad break timestamps through automated scene break detection. The computer vision model independently analyzes content characteristics and makes decisions about optimal ad insertion points without requiring human review or approval at each step.
2Productivity
If automated ad break detection is implemented, then processing efficiency and scalability are improved, but system complexity and computational resources increase
Solution Approach 1:
The patent segments the ad break detection process into distinct modular components: frame extraction, feature detection, scene break identification, and timestamp generation. Each component handles a specific aspect of the analysis, allowing the system to process complex video content through a series of simpler, specialized steps that can be independently optimized and maintained.
Solution Approach 2:
The system introduces intermediate data structures and processing layers between the raw video input and final ad break timestamps. Scene break detections serve as intermediaries that bridge visual content analysis and advertising delivery decisions, while audio break detections and metadata provide additional intermediate signals that refine the final timestamp selection.
3Adaptability or versatility
If multiple advertising channels with dynamic content are created, then advertising revenue and engagement are improved, but content management complexity and delivery coordination increase
Solution Approach 1:
The system performs preliminary action by pre-processing video content to generate ad break timestamps before the actual advertising delivery occurs. This advance preparation creates a structured framework that enables multiple advertising channels to be configured and managed independently, as the foundational scene break data is already available to guide all subsequent ad insertion operations.
Solution Approach 2:
The ad break detection system provides universal functionality that serves multiple advertising channels simultaneously. The same scene break detection infrastructure and timestamp generation mechanism support various ad formats, targeting strategies, and delivery methods, allowing a single system to manage diverse advertising requirements without requiring separate processing pipelines for each channel.
Data Source
AI summary
A system and method for ad break detection, including: a computer processor; a scene break detection service executing on the computer processor and comprising functionality to (i) receive a request for ad break detection on a media item, (ii) perform audio break detection on an audio component of the media item to obtain a set of audio break timestamps, (iii) identify a set of video break timestamps, each corresponding to at least one frame of a video component of the media item, (iv) identify a set of candidate ad break timestamps corresponding to instances of the set of the audio break timestamps and the set of video break timestamps within a predefined proximity, (v) execute a computer vision model to generate a scene break score for each candidate ad break timestamp, and (vi) select a final set of ad break timestamps based at least on the scene break scores.