Dynamic Video Cover Selection Using Adaptive Model Switching
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Solution Overview
Problem
The rapid growth of online video sharing has led to an abundance of video resources, making it challenging to efficiently process and extract relevant information from videos, as existing methods lack accuracy in determining dynamic covers that effectively represent video content.
Innovation Solution
A method and apparatus for processing videos that involve selecting a preset model from multiple models, determining output data based on the video and model, and iteratively reselecting models until the output data meets specific conditions to generate a dynamic cover, utilizing models like face recognition, behavior recognition, and content scoring to accurately represent video content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single preset model is used to determine dynamic cover, then the processing speed is fast, but the accuracy of video content representation is insufficient
Solution Approach 1:
The system dynamically selects different preset models based on video characteristics and conditions. Instead of using a fixed single model or exhaustively trying all models, the system adapts model selection to the specific video content and requirements, achieving high accuracy without excessive complexity
Solution Approach 2:
The system changes parameters such as model selection based on video content analysis and condition evaluation. By adjusting which model is applied according to the specific video characteristics and preset conditions, the system achieves accurate dynamic cover determination while maintaining reasonable processing complexity
2Measurement precision
If multiple models are exhaustively tried to find the best fit, then the accuracy improves, but the processing time increases
Solution Approach 1:
The system performs preliminary analysis of video characteristics and evaluates which preset model is most suitable before applying the model. This preliminary action avoids exhaustively trying all models, reducing processing time while still achieving high accuracy by selecting the most appropriate model in advance
Solution Approach 2:
The system uses condition evaluation as feedback to determine whether to continue with the current model or switch to another preset model. This feedback mechanism allows the system to efficiently navigate model selection without exhaustive searching, balancing accuracy and processing time
3Productivity
If simple video processing methods are used, then the processing speed is fast, but the ability to extract relevant information is limited
Solution Approach 1:
The system segments the video processing task into multiple stages: acquiring video data, analyzing characteristics, evaluating conditions, selecting appropriate preset models, and generating dynamic covers. This segmentation allows efficient processing at each stage while maintaining high information extraction quality through specialized model selection
Data Source
AI summary
A method, apparatus, and electronic device for processing a video, a medium and a product are presented. An implementation of the method includes: acquiring a target video; selecting, from at least one preset model, a preset model as a target model; determining output data of the target model based on the target video and the target model; reselecting, in response to determining that the output data does not meet a condition corresponding to the target model, another preset model as the target model from the at least one preset model until the output data of the target model meets the condition corresponding to the target model; and determining, based on the output data, a dynamic cover from the target video.