Video Insertion Zone Detection Using Shape and Color Metadata
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Solution Overview
Problem
Current methods for inserting advertising components into video material are time-consuming and inefficient, often requiring human operators to manually identify suitable zones, and existing automated processes are frame-based, which is not scalable for large volumes of video content.
Innovation Solution
An apparatus and method for automatically detecting and tracking insertion zones within pre-recorded video material based on shape, size, duration, movement, and color, allowing for the automated placement of additional material, such as advertising, by generating metadata that defines attributes of candidate zones and enabling seamless integration with the video content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human operators manually identify suitable zones for advertising insertion, then the precision of zone selection is improved, but the productivity and processing time are significantly reduced
Solution Approach 1:
The system creates a digital model (metadata) of the video content that captures scene structure, object positions, and temporal information. This model serves as a copy that can be processed automatically to identify insertion zones, eliminating the need for manual frame-by-frame analysis while maintaining accurate zone detection through structured data representation
Solution Approach 2:
The system performs preliminary analysis of video content to generate scene metadata, object tracking information, and temporal structure data before advertising insertion is required. This pre-processing creates a ready-to-use model that enables rapid automated identification of suitable zones without requiring manual intervention during the actual advertising placement process
2Extent of automation
If automated processes use frame-by-frame detection to find suitable placement areas, then the automation level is improved, but the processing complexity and time consumption increase
Solution Approach 1:
The system segments video analysis into distinct components: scene detection, object tracking, temporal structure analysis, and zone identification. Each component processes specific aspects of the video and generates structured metadata, which simplifies the overall automation process by breaking down complex frame-by-frame analysis into manageable, independent tasks that can be processed efficiently
Solution Approach 2:
The system transitions from analyzing video in the spatial dimension (frame-by-frame pixel analysis) to analyzing it in the temporal and semantic dimensions through metadata generation. By creating a structured representation that includes scene boundaries, object trajectories, and temporal relationships, the system enables automated zone identification without requiring exhaustive frame-by-frame processing
3Reliability
If advertising components are placed strategically within shots during recording, then the advertising effectiveness is improved, but the adaptability to different markets and the ability to insert ads into existing content is reduced
Solution Approach 1:
The system performs preliminary analysis of video content to identify and model suitable insertion zones with precise temporal and spatial metadata before advertising content is selected or customized for different markets. This pre-characterization of zones enables rapid adaptation to different market requirements by allowing different advertising content to be inserted into the same identified zones without re-analysis
Solution Approach 2:
The system identifies specific local regions within video frames that are suitable for advertising insertion, characterizing each zone's spatial position, temporal duration, and visual properties. This localized characterization allows different advertising content to be tailored for different markets while maintaining appropriate placement in each specific zone, enabling both high advertising impact and market-specific adaptability
Data Source
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AI summary
An apparatus and method for automatic detection of insertion zones within pre-recorded video material are provided. The apparatus includes a video analysis unit configured to automatically determine at least one candidate insertion zone within the pre-recorded video material suitable for receiving additional material to be inserted and configured to generate zone meta data defining attributes of the insertion zone, and an insertion module configured to receive additional material for insertion and arranged to generate an output representative of the pre-recorded video material with the additional material placed on an insertion zone such that the additional material adopts the image attributes of the insertion zone as the pre-recorded video material is played. The automatic determination of at least one insertion zone is based on one or more of feature shape, size, duration, movement, color.