Key Person Detection in Immersive Video Using Graph Attention Networks
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Solution Overview
Problem
Current methods for detecting key persons in immersive video, such as in sporting events, rely on manual camera operation which is expensive and not scalable, limiting the ability to provide an immersive experience by tracking and focusing on key players in real-time.
Innovation Solution
A system that uses a camera array and graph attention networks to detect key persons by identifying predefined formations and generating feature vectors, which are then used to classify key players through a graph attentional network, allowing for real-time tracking and virtual view generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual camera operation is used to track key players, then the quality of fan engagement and immersive experience is improved, but the cost and scalability deteriorate
Solution Approach 1:
The system enables automated detection and tracking of key persons through self-service mechanisms. The graph attention network automatically identifies key persons based on formation patterns and visual features without requiring manual camera operation, thereby maintaining engagement quality while eliminating the scalability limitations of manual approaches
Solution Approach 2:
The patent replaces the mechanical system of manual camera operation with an automated computer vision system. The graph attention network and formation detection algorithms substitute human operators, enabling the system to scale across multiple cameras and scenes while maintaining consistent key person detection and tracking performance
2Productivity
If automated detection systems are implemented, then scalability and operational efficiency are improved, but the complexity of the system increases
Solution Approach 1:
The system segments the complex detection task into distinct functional modules: formation detection module that identifies predefined player formations, graph attention network that processes spatial relationships, and key person detection module that identifies specific individuals. This segmentation manages complexity by organizing functions into separate, manageable components that can be processed independently
Solution Approach 2:
The patent introduces intermediate processing stages between raw video input and key person identification. The formation detection acts as an intermediary that filters and structures data before it reaches the graph attention network, which in turn prepares processed features for the final key person detection module, thereby managing system complexity through staged processing
3Reliability
If real-time tracking of multiple key persons is achieved, then the immersive user experience is improved, but the computational resources and processing time increase
Solution Approach 1:
The system performs preliminary detection of formation patterns and spatial relationships before final key person identification. The graph attention network pre-processes visual data to extract meaningful features and relationships, preparing the data structure in advance for faster final detection and tracking decisions, thereby reducing real-time processing delays
Solution Approach 2:
The patent dynamically adjusts detection parameters and processing depth based on scene context and formation types. The system modifies its operational parameters to optimize the balance between tracking accuracy and processing speed, enabling real-time performance by adapting computational resources to the specific requirements of each detection scenario
Data Source
AI summary
Techniques related to key person recognition in multi-camera immersive video attained for a scene are discussed. Such techniques include detecting predefined person formations in the scene based on an arrangement of the persons in the scene, generating a feature vector for each person in the detected formation, and applying a classifier to the feature vectors to indicate one or more key persons in the scene.