Video Insertion Zone Prediction Model
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Solution Overview
Problem
The process of identifying and evaluating opportunities for digital object insertion in videos is time-consuming and resource-intensive, especially for long videos like films or TV episodes, due to the manual or computational overheads involved in analyzing potential insertion zones and assessing their value.
Innovation Solution
A method and system that use video analysis techniques to create a prediction model for determining insertion zone metadata, allowing for the prediction of insertion zone characteristics in new videos based on categorical metadata, which reduces the need for detailed analysis of videos with insufficient potential for digital object insertion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed analysis is performed to identify and evaluate digital object insertion opportunities in videos, then the accuracy and quality of insertion zone identification is improved, but the time consumption and computational resources increase significantly
Solution Approach 1:
The system performs preliminary analysis by extracting categorical metadata from videos before conducting detailed insertion opportunity evaluation. This preliminary categorization allows the system to quickly assess videos and determine which ones warrant detailed analysis, thereby reducing overall time consumption while maintaining identification accuracy for promising candidates.
Solution Approach 2:
The analysis process is segmented into multiple stages: first, rapid categorical metadata extraction and video classification; second, targeted detailed analysis only for videos predicted to have insertion opportunities. This segmentation allows the system to apply different levels of analysis intensity to different videos, optimizing the balance between accuracy and time efficiency.
2Reliability
If comprehensive video analysis is conducted to evaluate all potential insertion zones, then the completeness of insertion opportunity identification is improved, but the computational overhead increases substantially
Solution Approach 1:
The system performs preliminary classification using categorical metadata extraction before conducting resource-intensive detailed analysis. This preliminary action filters out videos unlikely to contain valuable insertion opportunities, thereby reducing overall computational overhead while maintaining reliability through subsequent detailed analysis of only the most promising candidates.
Solution Approach 2:
The system applies different analysis qualities to different videos based on their categorical characteristics. Videos with metadata indicating high potential for insertion opportunities receive comprehensive detailed analysis, while others receive only preliminary assessment. This local quality approach ensures complete identification of opportunities in relevant videos while conserving computational resources on less promising content.
3Adaptability or versatility
If manual analysis is used to identify insertion opportunities, then the flexibility and adaptability of evaluation is improved, but the labor intensity and time consumption increase
Solution Approach 1:
The system introduces an intermediary automated classification layer that uses categorical metadata extraction to pre-screen videos before they undergo detailed evaluation. This intermediary process maintains the flexibility and adaptability of human-like evaluation judgment while dramatically increasing analysis throughput by filtering out videos that would not benefit from detailed manual or computational analysis.
Solution Approach 2:
The system replaces purely manual analysis with a hybrid approach where automated metadata extraction and classification algorithms handle the initial screening process. This substitution of mechanical/computational processes for manual labor increases productivity while preserving evaluation flexibility through configurable classification criteria and selective detailed analysis.
Data Source
AI summary
Aspects of the present disclosure aim to improve upon methods and systems for the incorporation of additional material into source video data. In particular, the method of the present disclosure may use a pre-existing corpus of source video data to produce, test and refine a prediction model for enabling the prediction of the characteristics of placement opportunities. The model may be created using video analysis techniques which obtain metadata regarding placement opportunities and also through the identification of categorical characteristics relating to the source video which may be provided as metadata with the source video, or obtaining through image processing techniques described below. Using the model, the method and system may then be used to create a prediction of insertion zone characteristics for projects for which source video is not yet available, but for which information corresponding to the identified categorical characteristics is known.