Augmented Video RGBA Selection for Sports Event Analytics
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Solution Overview
Problem
Existing systems struggle to effectively handle and analyze vast volumes of sporting event data, including difficulties in transforming X, Y, Z data into meaningful insights, identifying relevant events, and visualizing results, lacking tools for mining and presenting valuable information.
Innovation Solution
A system and method for combining video content with augmentations to produce augmented video, utilizing bounding boxes and RGBA values for user selection, and integrating machine learning for spatiotemporal pattern recognition and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If video content is combined with multiple augmentations, then the quantity and quality of information presented is improved, but the device complexity increases
Solution Approach 1:
The system segments the video analysis process into multiple independent modules: object detection, event recognition, metric extraction, and visualization. Each module handles specific aspects of the data, allowing complex information to be processed through manageable stages rather than monolithic processing, thus reducing overall system complexity while maintaining information completeness
Solution Approach 2:
The patent implements nested data structures where bounding boxes contain object information, which contains event data, which contains metrics. This nested organization allows multiple levels of information to be stored and processed efficiently, enabling comprehensive data presentation without proportionally increasing system complexity
2Measurement precision
If machine learning algorithms are applied for pattern recognition, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on historical sporting data and pre-defining pattern templates. This allows the models to make accurate pattern recognitions during live events without requiring complex real-time training, thus achieving high measurement precision while limiting processing complexity to predetermined operations
Solution Approach 2:
The patent introduces intermediate representation layers between raw video data and final analytics. Machine learning models process video frames into intermediate feature representations, which are then transformed into meaningful patterns and metrics. This intermediary processing layer simplifies the overall system by breaking down complex pattern recognition into manageable transformations
3Ease of operation
If bounding boxes and RGBA values are used for user selection, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The system uses color-coded bounding boxes and RGBA value overlays to represent different objects, events, and metrics visually. Users can distinguish between various elements through color changes and transparency levels, enabling intuitive interaction without complex controls. The visual encoding of information through color and transparency simplifies user selection while managing interface complexity
Data Source
AI summary
Data processing systems and methods are disclosed for combining video content with one or more augmentations to produce augmented video. Objects within video content may have associated bounding boxes that may each be associated with respective RGB values. Upon user selection of a pixel, the RGBA value of the pixel may be used to determine a bounding box associated with the RGBA value. The client may transmit an indicator of the determined bounding box to an augmentation system to request augmentation data for the object associated with the bounding box. The system then uses the indicator to determine the augmentation data and transmits the augmentation data to the client device.