Automatic Video Surface Detection for Seamless Ad Insertion
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Solution Overview
Problem
Conventional systems for inserting ads into videos require extensive human intervention, significant data processing, and computational power, and often violate digital rights management, making them inefficient and insecure.
Innovation Solution
An end-to-end video augmentation system using computer vision and object recognition techniques for automatic detection of surfaces in video frames, allowing seamless in-video ad insertion while ensuring content owner and advertiser requirements are met, and maintaining digital rights compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual human annotation is used to identify and insert ads into video frames, then ad placement precision can be ensured, but the process requires extensive human intervention and time consumption
Solution Approach 1:
The system enables automatic surface detection and ad insertion without human intervention. The computer vision system autonomously identifies surfaces in video frames, matches them with ad inventory, and performs insertion operations automatically, eliminating the need for manual annotators while maintaining precision through algorithmic consistency
Solution Approach 2:
Manual human annotation processes are replaced with automated computer vision algorithms. The system uses machine learning models to detect surfaces, extract features, and determine ad placement locations, substituting human cognitive and manual operations with automated computational processes that operate faster and more consistently
2Productivity
If extensive data processing is performed to detect surfaces and insert ads automatically, then productivity increases, but computational power requirements and system complexity increase significantly
Solution Approach 1:
The complex ad insertion system is divided into distinct modular components: surface detection module, feature extraction module, ad matching module, and insertion module. Each component handles a specific aspect of the process, allowing independent optimization and reducing overall system complexity while enabling parallel processing to maintain high productivity
Solution Approach 2:
The system performs preliminary surface detection and feature extraction before ad matching and insertion. By pre-processing video frames to identify and characterize surfaces in advance, the system reduces the computational burden during the actual ad insertion phase, enabling faster overall processing while maintaining accuracy
3Reliability
If conventional manual systems are used for ad insertion, then digital rights management compliance can be maintained through human judgment, but the process lacks automation and efficiency
Solution Approach 1:
The system incorporates feedback mechanisms where ad insertion decisions are validated against DRM rules and content owner preferences. The automated system monitors compliance status throughout the process and can reject or modify insertions that would violate digital rights management requirements, ensuring reliability while maintaining automation
Solution Approach 2:
An intermediary compliance layer is introduced between the automated ad insertion process and the final output. This intermediary component verifies that each ad insertion operation adheres to DRM policies and content owner specifications, acting as a mediator that enables automation while preserving legal and ethical compliance
Data Source
AI summary
Disclosed are systems and methods for improving interactions with and between computers in content providing, searching and/or hosting systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel framework for performing automatic detection of surfaces in video frames resulting in the creation of a seamless in-video ad experience for viewing users. The disclosed framework operates by leveraging available surfaces in videos to show advertisements in compliance with publisher protection, compliance and policy in a fully automatic, end-to-end solution. The disclosed framework evidences a streamlined, automatic and computationally efficient process(es) that modifies digital content at the surface level within the frames of the content in compliance with the digital rights of the owners of the content being merged via the disclosed augmentation.