Gesture-Based Video Navigation for Spatiotemporal Pattern Recognition
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Solution Overview
Problem
Current systems for analyzing live and recorded video feeds, particularly in sports, face challenges in handling and interpreting vast volumes of data, transforming XYZ motion data into meaningful insights, and visualizing results effectively, lacking tools for comprehensive data mining and analysis.
Innovation Solution
A system utilizing a technology stack that includes a customization layer for analytics, an interaction layer for real-time interaction, a visualizations layer for dynamic displays, and an analytics layer with AI tools for pattern recognition and machine learning, enabling spatiotemporal analysis and presentation of insights through gesture-based navigation and interactive visualizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional scouting methods are used to evaluate sporting information, then human expertise and intuition can be applied, but the volume of information that can be evaluated is limited and time-consuming
Solution Approach 1:
The patent replaces manual scouting methods with automated computer vision systems and machine learning algorithms. The system automatically captures, processes, and analyzes sporting event data through digital imaging and computational algorithms, eliminating the need for human scouts to manually observe and record information. This substitution dramatically increases information evaluation capacity while reducing the time required for analysis.
Solution Approach 2:
The patent introduces an intermediate layer of data processing systems that bridge raw video footage and actionable insights. The system includes multiple processing layers: data capture from video feeds, automated tracking of athletes and objects, feature extraction algorithms, and presentation systems that translate complex data into meaningful metrics. This intermediary processing infrastructure enables efficient handling of vast information volumes.
2Quantity of substance
If automated systems capture and encode event information with high detail, then comprehensive data is available for analysis, but difficulty handling and transforming the data into meaningful insights increases
Solution Approach 1:
The patent segments the complex data processing task into distinct functional layers: data capture layer (video feeds from multiple cameras), data processing layer (tracking algorithms, feature extraction), analytics layer (pattern recognition, metric calculation), and presentation layer (visualizations, reports). Each layer handles specific aspects of data transformation, making the overall system more manageable and scalable despite the large volume of input data.
Solution Approach 2:
The patent introduces intermediate data structures and processing frameworks that bridge raw captured data and final analytical insights. The system uses structured data formats, intermediate feature representations, and standardized communication protocols between processing modules. These intermediaries organize and normalize data flows, reducing complexity in transforming vast amounts of raw data into meaningful information.
3Loss of information
If comprehensive spatiotemporal analysis is performed on video feeds, then novel insights and metrics can be discovered, but the computational resources and processing time required increase
Solution Approach 1:
The patent performs preliminary processing and filtering of video data before comprehensive analysis. The system pre-processes video feeds by detecting and tracking relevant objects (athletes, ball, equipment), extracting key features (position, velocity, acceleration), and organizing data by event types. This preliminary action reduces the computational burden of subsequent detailed spatiotemporal analysis by focusing processing only on relevant data portions.
Solution Approach 2:
The patent implements a multi-level analysis approach where the system performs basic tracking and detection on all video data, then applies more computationally intensive spatiotemporal pattern recognition only to identified events of interest. The system balances comprehensive analysis with resource constraints by selectively applying different levels of processing intensity based on event significance and user needs.
Data Source
AI summary
A user interface for a media system supports using gestures, such as swiping gestures and taps, to navigate frame-synchronized video clips or video feeds. The detection of the gestures is interpreted as a command to navigate the frame-synchronized content. In one implementation, a tracking system and a trained machine learning system is used to generate the frame synchronized video clips or video feeds. In one implementation, video clips of an event are organized into storylines and the user interface permits navigation between different storylines and within individual storylines.