Video Augmentation via Machine Learning Region Detection
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Solution Overview
Problem
Existing video augmentation systems for sporting events require multiple cameras, specific camera placements, and equipment to detect in-venue signage, limiting flexibility and accuracy in identifying and overlaying advertisement content within video frames.
Innovation Solution
A computing system employing computer vision techniques and machine learning models to automatically identify and augment negative space or underutilized regions in video frames without the need for specific camera setups or in-venue equipment, allowing for real-time or delayed overlay of advertisement content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing video augmentation systems use multiple cameras and specific camera placements to detect in-venue signage, then detection accuracy is improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The system uses a single camera to capture video frames, then creates a digital copy or representation of the scene through computer vision algorithms. Machine learning models process these frames to identify candidate regions and advertisement content, replacing the need for multiple physical cameras with a virtual processing system that analyzes and reconstructs the scene information digitally.
Solution Approach 2:
The patent replaces the mechanical system of multiple physical cameras with specific placements with a computational system using a single camera combined with machine learning algorithms. The detection and identification functions are transferred from physical camera arrays to software-based image processing and neural network analysis, eliminating complex mechanical setups while maintaining detection capabilities.
2Reliability
If existing systems require in-venue equipment and specific camera placements, then detection reliability is improved, but adaptability deteriorates
Solution Approach 1:
The system is designed to be universal and adaptable to different venues and events using a single camera. The machine learning models are trained on diverse data to recognize various advertisement formats, signage types, and scene configurations across different sporting events and locations. This multi-functional approach allows the same system to reliably detect and augment advertisements in football stadiums, basketball arenas, and other venues without requiring venue-specific equipment configurations.
3Measurement precision
If manual identification of advertisement regions is used, then accuracy is improved, but productivity deteriorates
Solution Approach 1:
The system employs machine learning models that automatically perform region identification and advertisement detection without requiring manual intervention. The neural networks self-learn from training data and autonomously identify candidate regions, determine advertisement content, and generate augmentation masks. This self-service capability maintains high accuracy while dramatically increasing processing speed and productivity compared to manual methods.
Solution Approach 2:
The system transforms the identification task from a manual, time-consuming process to an automated computational process by changing the operational parameters. Machine learning models process multiple video frames simultaneously, analyzing visual features, patterns, and contextual information to identify advertisement regions with high accuracy. This parameter change from manual to automated processing maintains precision while enabling real-time or near-real-time operation.
Data Source
AI summary
Systems and methods are provided for identifying one or more portions of images or video frames that are appropriate for augmented overlay of advertisement or other visual content, and augmenting the image or video data to include such additional visual content. Identifying the portions appropriate for overlay or augmentation may include employing one or more machine learning models configured to identify objects or regions of an image or video frame that meet criteria for visual augmentation. The pose of the augmented content presented within the image or video frame may correspond to the pose of one or more real-world objects in the real world scene captured within the original image or video.