Multi-Camera Video Conferencing for Face and Object Framing
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Solution Overview
Problem
Users find it cumbersome to manually adjust multiple image sensors during a video conference to capture both their face and an object, leading to a dissuasion in using multiple sensors.
Innovation Solution
A system that automatically determines the intended element (face or object) using neural networks and distance sensors, adjusting image settings and presentation arrangements to optimize the display of both elements with minimal user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users manually adjust multiple image sensors to capture both face and object, then image capture capability is improved, but operation complexity increases
Solution Approach 1:
The system automatically determines which image sensor should capture the face and which should capture the object without user intervention. The processor autonomously analyzes images from multiple sensors, determines prominence of faces and objects, and configures sensor assignments and presentation arrangements automatically, eliminating the need for manual user adjustment.
Solution Approach 2:
The system dynamically changes image sensor parameters (such as focus, exposure, and field of view) and presentation arrangement parameters based on real-time analysis of image content. The processor adjusts these parameters automatically according to the detected prominence of faces and objects, allowing the system to adapt to different scenarios without manual reconfiguration.
2Reliability
If multiple image sensors are used to capture both face and object, then presentation quality is improved, but device complexity increases
Solution Approach 1:
The system employs multiple image sensors that can serve different functions dynamically. The same image sensors can be assigned to capture either faces or objects based on real-time needs, rather than having dedicated sensors for each purpose. This multi-functional approach improves presentation quality while managing device complexity through flexible resource allocation.
Solution Approach 2:
The system dynamically reconfigures the roles and settings of image sensors based on the current video conference context. The processor continuously monitors image content and adjusts sensor assignments, focus settings, and presentation arrangements in real-time, transforming a static multi-sensor system into a dynamic one that adapts to changing presentation needs.
3Measurement precision
If manual adjustment of image sensors is required, then precise control is achieved, but user time consumption increases
Solution Approach 1:
The system implements a feedback loop where the processor continuously analyzes images from multiple sensors, determines the prominence of faces and objects, and automatically adjusts sensor assignments and presentation arrangements based on this analysis. This closed-loop feedback mechanism maintains precise control over image capture quality while eliminating the need for manual user adjustment and associated time consumption.
Data Source
AI summary
An example system includes a first image sensor, a second image sensor, and an electronic device. The electronic device includes a controller that is to compare positions of a human face and a non-human object in a first set of images captured by the first image sensor. In addition, the controller is to select a presentation arrangement of the first set of images and a second set of images captured by the second image sensor based on the comparison. Further, the controller is to output the first set of images and the second set of images to a video conferencing application in the selected presentation arrangement.