Interactive Video Augmentation via Spatiotemporal Segmentation
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Solution Overview
Problem
Current systems fail to effectively analyze and visualize vast amounts of data from live sporting events, such as NBA games, making it difficult to extract meaningful insights and provide tools for mining and analyzing sporting information.
Innovation Solution
A computer-implemented method and system for generating augmented video content by processing spatiotemporal data from video feeds, identifying semantic elements and contexts, and overlaying augmentations such as player statistics or advertising content, allowing for interactive visualization and user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If video content is enhanced with multiple semantic augmentations and interactive overlays, then viewer engagement and information density are improved, but system complexity and processing requirements increase
Solution Approach 1:
The system segments video processing into distinct modules: semantic element identification, context determination, augmentation generation, and rendering. Each module handles specific tasks independently, reducing overall system complexity while enabling comprehensive video enhancement with multiple augmentations.
Solution Approach 2:
The system introduces an intermediary processing layer that receives raw video data, identifies semantic elements, determines contexts, and generates appropriate augmentations. This intermediary layer acts as a mediator between the video stream and the final enhanced output, managing complexity through structured processing steps.
2Speed
If real-time processing of spatiotemporal data is performed to identify semantic elements and generate augmentations, then responsiveness and interactivity are improved, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-defining semantic element categories and augmentation templates before processing live video. This allows the system to quickly match current video content against pre-established frameworks, reducing real-time computational requirements while maintaining responsiveness.
Solution Approach 2:
The system applies local quality enhancement by processing and enhancing only specific regions of interest within the video frame where semantic elements are detected, rather than uniformly processing the entire video stream. This selective processing reduces computational resource consumption while maintaining high interactivity.
Data Source
AI summary
Data processing systems and methods are disclosed for augmenting video content with one or more augmentations to produce augmented video. Elements within video content may be identified by spatiotemporal indices and may have associated values. An advertiser can pay to have an augmentation added to an element that, for example, advertises the advertiser's goods and/or includes a link that, when activated, takes a user to the advertiser's website. Elements may have associated contexts that can be used to determine augmentations and element value, such as a position and/or current use of the element.