Dynamic Camera Presets for Video Conferencing
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Solution Overview
Problem
Current video conferencing systems require manual user adjustment of camera pan, tilt, and zoom to focus on specific locations, which can be time-consuming and inconvenient.
Innovation Solution
A method that uses machine learning models to generate dynamic camera presets by identifying bounding boxes and classification values in images, allowing users to select tags that automatically adjust the camera view settings to focus on desired areas without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual controls are used to adjust camera pan, tilt, and zoom, then the user can precisely control the camera view, but the process becomes time-consuming and inconvenient
Solution Approach 1:
The system pre-generates multiple candidate camera views (presets) by analyzing the image and creating bounding boxes around objects of interest before the user needs to adjust the camera. When the user interacts with the system, these pre-computed views are immediately available for selection, eliminating the time-consuming manual adjustment process while maintaining precise control capability.
2Productivity
If automated camera presets are generated using machine learning, then camera adjustment time is reduced, but the system complexity increases
Solution Approach 1:
The patent introduces an image analysis component as an intermediary that processes the captured image to identify objects of interest and generate candidate bounding boxes. This intermediary layer translates the complex machine learning object detection task into simple geometric regions that can be easily used for camera preset generation, thereby improving productivity while managing system complexity through modular design.
Data Source
AI summary
A method implementing dynamic camera presets. An image is received. A list of bounding boxes and a list of classification values are generated from the image. A set of tags, for the image, are generated from the list of bounding boxes and the list of classification values. A tagged image incorporating the set of tags into the image is presented. In response to a user input, a tag selection identifying a tag of the set of tags is received. View settings are adjusted to focus on a bounding box, from the list of bounding boxes, corresponding to the tag selection. An adjusted image is presented using the view settings.