Automatic Video Summarization Using Spatio-Temporal Clip Ranking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manually fragmenting videos to create highlights is time-consuming and prone to human error, requiring specialized knowledge, and there is a need for automated systems to generate highlights from video programs based on specific types of events.
Innovation Solution
A neural network is configured using a machine learning module and a set of videos to automatically recognize and generate highlights by training on positive and negative clips, employing a three-dimensional spatio-temporal encoder and a ranking network with self-attention mechanisms to determine clip relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual fragmenting is used to create video highlights, then the quality and accuracy of highlight selection can be ensured through human expertise, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent replaces the manual mechanical process of video analysis and highlight selection with an automated neural network system. The system uses deep learning models to automatically identify and segment highlight moments in videos, substituting human expert analysis with machine-based automated detection, thereby eliminating the time-consuming manual process while maintaining selection quality through trained algorithms
Solution Approach 2:
The patent enables the video processing system to perform highlight selection autonomously without human intervention. The neural network is trained to independently identify highlight moments, perform temporal segmentation, and generate highlight clips automatically, making the system self-sufficient in the highlight generation process and eliminating the need for manual human labor
2Reliability
If manual video analysis is performed by experts, then accurate identification of important fragments can be achieved, but the process becomes human-dependent and prone to human error
Solution Approach 1:
The patent applies preliminary action by training the neural network model in advance with large datasets of labeled video content and highlight annotations. This pre-training phase enables the system to learn patterns and features of highlight moments before actual deployment, ensuring consistent and reliable highlight identification when the system processes new videos, while the complexity is managed through systematic model development
Solution Approach 2:
The patent replaces human expert analysis with an automated neural network system that provides consistent, reproducible results. The system uses deep learning architectures including attention mechanisms and temporal modeling to reliably identify highlight moments without human variability, eliminating human error while managing technical complexity through established machine learning frameworks
3Productivity
If automated systems are implemented for highlight generation, then productivity and speed can be improved, but the system requires complex machine learning models and training data
Solution Approach 1:
The patent segments the complex highlight generation task into distinct processing stages: video preprocessing, feature extraction, highlight moment detection, temporal boundary identification, and clip compilation. This segmentation allows the system to handle each subtask with specialized neural network components, improving overall productivity while managing complexity through modular architecture design
Solution Approach 2:
The patent introduces temporal dimension modeling by processing video data across multiple time steps and using recurrent neural networks or temporal attention mechanisms. This dimensional approach enables the system to capture temporal patterns and dynamics in video content, achieving high-speed automated highlight generation while managing complexity through structured temporal processing
Data Source
AI summary
Systems and methods for automatic generation of highlights of video. The system includes a video processor to select one type of the video to be analyzed and split video clips from the video. The video processor recognizes positive clips, negative clips, and auxiliary clips. A spatio-temporal encoder is configured to select, from the recognized clips, a main positive clip, a main negative clip, and auxiliary positive and negative clips, and generate a three-dimensional (3D) embedding vector of each clip. The selected clips are processed by a ranking network having a self-attention layer. The self-attention layer, using a query head, a key head and the value head produces self-attention resultant vector on which an activation function is performed. A rank value is thus obtained for the selected clip. Based on the rank value, video highlights are generated.