Foveated Multi-Camera Activation for Lower-Power Imaging
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Solution Overview
Problem
Existing multi-camera systems, particularly in head-mounted devices, consume significant power due to all cameras running continuously, and their image signal processing pipelines are complex and inefficient, especially for AI applications.
Innovation Solution
Implementing a multi-camera system with on-demand activation using a Region-of-Interest (ROI) Prediction Unit, Activation Control Unit, and Foveated View Rendering Unit, which selectively activates detail cameras based on gaze data, object tracking, and audio inputs to generate foveated images with high-resolution regions of interest and lower resolution elsewhere.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all cameras run continuously to capture high resolution images, then image quality is improved, but power consumption increases significantly
Solution Approach 1:
The system divides the camera array into multiple independently controllable camera modules, each capable of being activated or deactivated based on local requirements. This segmentation allows selective activation of only those cameras needed to capture the current region of interest at high resolution, rather than running all cameras continuously.
Solution Approach 2:
The system dynamically adjusts which cameras are active based on real-time gaze data, head pose, and detected objects of interest. The activation state of each camera changes dynamically according to the user's attention focus and scene requirements, optimizing the balance between image quality and power consumption.
2Measurement precision
If all cameras run continuously to capture high resolution images, then image quality is improved, but device heat increases
Solution Approach 1:
By segmenting the camera system into independently controllable modules, only the necessary subset of cameras is activated to capture the current region of interest. This reduces the total number of active cameras, thereby lowering overall heat generation while maintaining sufficient image quality for the areas that matter most to the user.
Solution Approach 2:
The system activates only the minimum necessary number of cameras (partial action) required to capture the current region of interest at high resolution, rather than activating all cameras. This partial activation approach provides sufficient image quality for the user's focus area while avoiding the excessive heat generation that would result from running all cameras simultaneously.
3Measurement precision
If all cameras run continuously to capture high resolution images, then image quality is improved, but data transmission requirements increase
Solution Approach 1:
The system segments the imaging task by activating only those cameras that capture regions of interest, rather than having all cameras continuously capture and transmit full-resolution data. This reduces the total volume of high-resolution image data that needs to be processed and transmitted, while still maintaining high quality images for the relevant areas.
Solution Approach 2:
The system extracts and prioritizes only the regions of interest (based on gaze data, head pose, and object detection) for high-resolution capture and transmission. Non-critical regions are captured at lower resolution or not captured at all, thereby reducing the overall data transmission requirements while maintaining image quality where it matters most to the user.
4Measurement precision
If image processing is performed on entire high resolution images, then image quality is maintained, but compute resources are consumed excessively
Solution Approach 1:
The system segments the image processing workload by focusing computational resources only on the regions of interest captured by the activated cameras. Rather than processing entire high-resolution images from all cameras, the system processes only the relevant regions at high resolution, significantly reducing compute resource consumption while maintaining image quality for the areas that matter most.
Solution Approach 2:
The system extracts and processes only the essential regions of interest at full computational capacity, while using simplified processing or lower resolution for non-critical areas. This selective processing approach maintains image quality for the user's focus areas while avoiding excessive compute resource consumption on the entire image dataset.
Data Source
AI summary
A multi-camera system includes a guide camera, a plurality of detail cameras, and processing logic. The guide camera configured to capture a guide image in a first field of view (FOV). The plurality of detail cameras have narrower field of views (FOVs) than the first FOV of the guide camera. The processing logic is configured to selectively activate one or more of the detail cameras to capture one or more detail images in response to the guide image.