Dynamic Ad Stitching via Black Frame Detection
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Solution Overview
Problem
The challenge lies in efficiently monetizing on-demand streaming content, as current methods require manual insertion of advertisements, which are time-consuming, expensive, and prone to errors due to the need for operators to manually tag video frames for advertisement placement.
Innovation Solution
A system and method for dynamically stitching advertisements into streaming content by automatically detecting sequential black frames to identify insertion points, allowing for real-time advertisement insertion without prior user input, using a server system that scans content for black frames and triggers advertisement playback at registered points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual insertion points are used to flag advertisement servers, then advertisement placement can be controlled, but the process becomes time-consuming and expensive
Solution Approach 1:
The system performs self-service by automatically detecting insertion points through scanning for black frames in the video content, eliminating the need for manual operator intervention to flag advertisement insertion points while maintaining reliable control over ad placement
Solution Approach 2:
The manual mechanical process of operators viewing and flagging video frames is replaced with an automated electronic scanning system that detects black frames through algorithmic analysis, substituting human labor with computational processing
2Measurement precision
If manual tagging of video frames is performed, then accurate insertion points can be identified, but the process is expensive and prone to error
Solution Approach 1:
The system automatically identifies insertion points by scanning for black frames without requiring manual operator tagging, reducing human error while maintaining accurate identification through consistent algorithmic detection criteria
Solution Approach 2:
The system changes the detection parameter from manual visual assessment to automated black frame detection, using objective computational criteria to identify insertion points with consistent precision across all video content
3Reliability
If advertisements are inserted manually, then placement control is maintained, but productivity decreases
Solution Approach 1:
The system performs self-service by automatically detecting insertion points and triggering advertisement playback without manual intervention, significantly improving productivity while maintaining reliable control through automated detection algorithms
Solution Approach 2:
The system performs preliminary scanning of video content to pre-identify black frames and register insertion points before advertisement playback is triggered, enabling efficient automated ad insertion that improves overall processing speed and productivity
Data Source
AI summary
Receiving a first portion of a live stream of a content item being either prerecorded or being captured from a live event, the content item comprising or to comprise a set of stream-enabled video segments, the first portion of the live stream comprising a first subset of stream-enabled video segments. Identifying insertion point(s) within at least one stream-enabled video segment. Generating a first playlist based on the one or more identified insertion points, the first playlist associated with at least a first sub-subset stream-enabled video segments. Providing the first playlist to a consumer system. Receiving a second portion of the live stream comprising a second subset of stream-enabled video segments. Receiving a second playlist associated with the second portion of the live stream. Updating the first playlist based on the second playlist, and providing the updated first playlist to the consumer system.