Video Highlight Ranking for Specific Duration and Event Labels
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manually generating video highlights is time-consuming and prone to human error, especially when specific durations and types are required, making the process inefficient and unreliable.
Innovation Solution
A system and method using a pre-configured ranking neural network (RankNet) to automatically generate video highlights of a specific duration by splitting videos into fragments, ranking them, and selecting fragments based on spatio-temporal encoding to meet the desired duration and type criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual fragmenting is used to create video highlights, then the highlights can be customized with specific duration and content type, but the process becomes time-consuming and tedious
Solution Approach 1:
The patent replaces the manual mechanical process of video fragmenting with an automated computer-based system. The system automatically divides video into fragments, scores them using machine learning models, and assembles highlights without human intervention, thus eliminating the time-consuming manual operation while maintaining customization capabilities through programmable parameters.
Solution Approach 2:
The system performs self-service by automatically evaluating and selecting video fragments based on pre-defined criteria and scoring mechanisms. The automated scoring system independently identifies important moments without requiring human judgment, enabling the system to serve itself in the highlight generation process while adhering to specified duration and content type requirements.
2Reliability
If manual assessment by experts is used to identify important fragments, then the highlights can be accurate and reliable, but the process becomes human-dependent and prone to human errors
Solution Approach 1:
The patent replaces human expert assessment with automated machine learning-based scoring systems. Multiple models including action recognition models, event detection models, and ranking models work together to objectively evaluate video fragments, eliminating human dependency and reducing subjective errors while maintaining or improving selection accuracy through consistent algorithmic evaluation.
Solution Approach 2:
The system implements feedback mechanisms where scoring results from multiple models are aggregated and used to iteratively improve fragment selection. The ranking model receives feedback from action recognition and event detection models, adjusting fragment scores based on cumulative evidence, which enhances reliability through multi-layered validation rather than single human judgment.
3Adaptability or versatility
If manual selection of clips and duration is used, then the highlights can be tailored to specific objectives, but the process becomes complicated and less reliable
Solution Approach 1:
The system maintains adaptability by allowing users to set various parameters such as target duration, fragment length, and scoring thresholds. These parameters can be adjusted based on different objectives (e.g., sports highlights vs. news clips), while the automated processing ensures consistent and reliable results regardless of parameter settings, eliminating the variability introduced by manual judgment.
Solution Approach 2:
The system performs preliminary actions by pre-training multiple machine learning models on domain-specific data before actual highlight generation. Action recognition models are trained on sport-specific actions, event detection models are prepared with domain knowledge, ensuring that when highlights are generated for specific objectives, the system already has the necessary expertise built-in, making the process both adaptable and reliable.
Data Source
AI summary
Systems and methods for generating video highlights with a certain label and a specific duration (SD) using a trained ranking neural network (RankNet). The system obtains a request for highlight generation, specific duration, and a specific label. The video is split into a set of fragments of pre-defined duration forming a sequence. Digital representation in a form of 3D spatio-temporal embedding is generated for each fragment by a spatio-temporal encoder. Using the embedding value, a rank of each fragment is identified by a trained Ranking Neural Network. Ranks are recorded into a data structure. A minimum number of fragments are selected to cover the SD using a criteria comprising comparing ranks of different fragments. A video highlight is generated from concatenated selected fragments with a truncation, if necessary.