Video Summarization Using Stochastic Game Models
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Solution Overview
Problem
Current video summarization methods for sports rely heavily on visual and audio saliency, failing to effectively capture the nuanced importance of various events and player interactions, leading to monotonous highlight reels and a lack of context-awareness in summarizing game events.
Innovation Solution
A method utilizing stochastic models and game context to assign quantitative scores to video segments based on the impact and likelihood of events, allowing for dynamic segmentation and summarization of videos by evaluating game states and events, incorporating continuous signals and Markov models to assess player and team performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If visual and audio saliency methods are used for video summarization, then the summarization process is simple and fast, but the context-awareness and nuanced evaluation of game events are insufficient
Solution Approach 1:
The patent introduces an intermediary layer between simple visual/audio analysis and final summarization. This intermediary consists of computational models that evaluate game states, player behaviors, and event contexts using domain knowledge. These models act as mediators that transform basic visual features into context-aware evaluations, resolving the contradiction by adding intermediate processing steps that enrich information without completely sacrificing efficiency.
Solution Approach 2:
The video summarization process is segmented into multiple independent modules: visual feature extraction, audio feature extraction, game state evaluation, player behavior analysis, and final segment scoring. Each module processes specific aspects independently, allowing the system to maintain computational efficiency while accumulating contextual information across different analysis layers.
2Loss of information
If computational models evaluating game states and player behaviors are used, then context-awareness and event importance evaluation improve, but system complexity increases
Solution Approach 1:
The patent develops universal computational models that can evaluate multiple aspects of game states simultaneously. A single game state evaluation model serves multiple functions: assessing player positions, determining event contexts, evaluating team strategies, and predicting game outcomes. This multi-functionality reduces the need for separate specialized models, thereby managing complexity while maintaining comprehensive context awareness.
Solution Approach 2:
The system manages model complexity by dynamically adjusting evaluation parameters based on game context. Instead of using fixed complex models for all situations, the system modifies evaluation parameters adaptively - for example, changing which player attributes are evaluated or which game states are monitored based on the current phase of play, thereby reducing unnecessary computational complexity while maintaining context awareness when needed.
3Measurement precision
If quantitative scoring of video segments is implemented, then highlight identification accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system applies partial scoring action by focusing computational resources on evaluating only the most critical game events and segments rather than uniformly scoring every video frame. The quantitative scoring is applied selectively to segments identified as potentially containing highlights based on preliminary analysis, thereby achieving high measurement precision for important events while reducing overall processing time through targeted evaluation.
Data Source
AI summary
There are provided methods and systems to generate a summary of a video by decomposing the video into segments automatically, where each segment has a quantitative score. The assigned scores to those segments can be generated using models that are quantitatively describing and/or evaluating the individual or group activities of the objects in the scene. The segments can be grouped based on their scores to generate a video summary. In an implementation, such a system can generate video summaries of a game based on the quantitative game models. Using a game model that assigns values to different game events and actions in a game, a set of most interesting, least interesting and neutral plays can be identified in the video and a highlight or lowlight reel generated. With adjusting the valuation of player actions and game events based on their impact on the game's result, the monotony in highlight reels can be avoided. The video segments can also be used to generate a playlist of the different plays in game ordered based on their assigned scores.