Eye-Tracking Video Network Bandwidth Control
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Solution Overview
Problem
Current video networks, especially those with wireless channels, are limited by bandwidth constraints, making it impractical to support multiple high-resolution cameras due to the high bandwidth requirements for transmitting high-resolution images at high frame rates and low compression rates, and users cannot effectively view all high-resolution images from multiple cameras simultaneously.
Innovation Solution
A system with multiple resolution cameras and an eye-tracking based control system that dynamically adjusts camera resolution based on user attention, allowing only the focused camera to transmit high-resolution images while others transmit at lower resolution, and a composite image module generates images to prioritize bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple high-resolution cameras are included in the video network to cover larger geographic area and increase number of views, then the surveillance coverage and viewing capability are improved, but the bandwidth requirement increases significantly making the system impractical for wireless networks
Solution Approach 1:
The video feed from each camera is segmented into multiple resolution versions (e.g., high-resolution and low-resolution streams). The system transmits only the necessary resolution segments based on current viewing requirements, allowing multiple cameras to be supported without proportionally increasing total bandwidth consumption.
Solution Approach 2:
Different parts of the video data are assigned different quality levels. High-resolution video is transmitted only for regions or time periods when actively viewed, while other regions use lower resolution. This allows the system to maintain comprehensive surveillance coverage while optimizing bandwidth usage based on local viewing demands.
2Measurement precision
If high-resolution images are transmitted from multiple cameras concurrently at high frame rate and low compression, then the image quality and detail are improved, but the bandwidth requirement exceeds the capacity of wireless channels and traditional Ethernet
Solution Approach 1:
The system dynamically adjusts video resolution and transmission quality based on real-time conditions such as active viewing status, camera importance, and network bandwidth availability. When a camera is not actively viewed, its transmission quality is reduced or suspended, freeing bandwidth for other cameras. This dynamic adaptation allows the system to maintain high image quality for viewed cameras while supporting multiple cameras within limited bandwidth capacity.
Solution Approach 2:
The system changes key transmission parameters including resolution, frame rate, and compression ratio based on current operational needs. Instead of transmitting all cameras at fixed high quality, parameters are adjusted so that viewed cameras receive high-resolution streams while non-viewed cameras use lower resolution, enabling support for more cameras within the same bandwidth constraints.
3Loss of information
If all camera images are transmitted at high resolution, then complete visual information is provided for all views, but it is inefficient for users to view all images and wastes valuable network bandwidth
Solution Approach 1:
The system transmits high-resolution video only for the subset of cameras that are currently being viewed, rather than transmitting high-resolution video for all cameras. This partial action approach maintains complete visual information for active views while avoiding the bandwidth waste of transmitting high-resolution data for inactive views. The system can quickly switch between different camera views as user attention changes.
Data Source
AI summary
A system. The system includes a computing device configured for communication with a plurality of multiple resolution cameras and with a display device. The computing device includes a composite image module. The composite image module is configured for receiving composite information from at least one of the multiple resolution cameras, and for generating a composite image based on the composite information.