Smart Video Thumbnail Cropping via Dynamic Feature Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video editing technologies face challenges in accurately cropping video thumbnails due to variations in video composition and camera work, often resulting in feature splitting or omission, as they rely on static cropping methods and narrow-focused feature detection approaches.
Innovation Solution
A smart cropping system that employs a multilevel feature extraction approach based on scene recognition, using a scene segmentation module to cluster frames, a feature processing module to extract and stack features, and a scoring module to generate a bounding box for accurate cropping, incorporating AI-based training models and online data for high-fidelity feature detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static cropping methods are used to simplify the cropping process, then ease of operation is improved, but manufacturing precision deteriorates due to feature splitting or omission
Solution Approach 1:
The patent transforms static cropping into a dynamic process by continuously analyzing video frames to identify features of interest and automatically adjusting the cropping region. The system dynamically determines the cropping area based on detected features such as faces, objects, or salient regions, allowing the crop to adapt to changing scene content rather than relying on fixed pre-defined regions.
Solution Approach 2:
The patent replaces manual static cropping operations with an automated computer vision-based system. Machine learning models and feature detection algorithms substitute for manual intervention, automatically identifying and tracking features of interest across frames to determine optimal cropping regions, thereby eliminating the need for manual viewpoint specification.
2Device complexity
If narrow-focused feature detection approaches are used to simplify the system, then device complexity is reduced, but measurement precision deteriorates due to false recognition
Solution Approach 1:
The patent combines multiple feature detection approaches and analysis methods into a unified system. It integrates various detection algorithms, multi-frame analysis, and contextual understanding to comprehensively identify features of interest. By merging these different computational approaches, the system achieves higher detection precision without requiring overly complex individual components.
Solution Approach 2:
The patent transitions from analyzing single frames in isolation to examining multiple frames temporally and spatially. By incorporating temporal dimension through multi-frame analysis and considering spatial relationships between detected features, the system improves feature detection accuracy and reduces false recognition while maintaining manageable computational complexity.
Data Source
AI summary
Disclosed is a system for automatic cropping of an image of interest from a video sample using smart systems. The image of interest is an image representative of the video sample, which includes desirable characteristics as required by the user, such as a person or object of focus, a specific aspect-ratio, preferred landmarks, information/time-stamps etc. The system for automatic cropping analyzes the video sample and its content to detect at least one image feature. The image feature is then classified based on importance and a potential test cropping area is determined based on the cumulative importance of features detected within each frame. The smart cropping systems and methods disclosed ensure that the most relevant aspects of a video sample are included within the image of interest.