Portable Video Stream Alignment via Visual Activity Detection
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Solution Overview
Problem
Existing technologies lack effective methods for aligning video streams generated by portable devices, such as smartphones, which are not adequately addressed by conventional solutions, hindering processes like generating analytics or augmented content from live events.
Innovation Solution
A method and system for automatically aligning video streams by identifying visually distinctive activities like game clocks, camera flashes, electronic displays, ball movements, and participant positions, using optical character recognition and computer vision algorithms to determine a time offset between streams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional alignment methods are used, then simple video streams may be processed, but complex video streams from portable devices cannot be effectively aligned
Solution Approach 1:
The patent segments the video stream analysis into multiple distinct visual feature types (game clocks, electronic displays, camera flashes, ball movements, participant positions). Each feature type is detected and processed independently through specialized algorithms, allowing the system to handle the complexity of portable device video streams while maintaining high alignment accuracy through multiple independent verification points.
2Measurement precision
If multiple visually distinctive activities are analyzed, then alignment accuracy improves, but processing complexity increases
Solution Approach 1:
The system dynamically selects and weights different visual feature types based on their detectability and reliability in the current video stream. The processing complexity adapts to the actual content of the video streams, focusing computational resources on the most informative features while ignoring redundant or unavailable ones, thus achieving high precision without excessive complexity.
3Measurement precision
If conventional alignment methods are used, then processing is simple, but alignment accuracy for diverse viewpoints is insufficient
Solution Approach 1:
The system performs preliminary detection and classification of visual features (game clocks, electronic displays, camera flashes, ball movements, participant positions) before the actual alignment computation. This preliminary action prepares structured data about distinctive activities in advance, enabling the subsequent time offset determination to proceed efficiently with pre-processed information, thus achieving both high accuracy and maintained productivity.
Data Source
AI summary
Methods and systems for determining a time offset between a first video stream and a second video stream that depict a sporting event. Depictions of a first kind of visually distinctive activity are identified in the first and second video streams. The time offset between the two video streams is determined at least in part by comparing the depictions of the first kind of visually distinctive activity in the first video stream with those in the second video stream.