AV System Cloud Video Latency via Local Processing
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Solution Overview
Problem
Current video conference systems using cloud communication lack efficient multi-person face and object recognition, and background processing capabilities, especially when dealing with multiple participants and complex environments, leading to latency and inconsistent user experiences across different platforms.
Innovation Solution
A system comprising an AV-system connected to a unified communication cloud server, equipped with functionality service software that performs real-time processing of video feeds, enabling multi-person face and object recognition, digital content integration, and image processing to generate a processed video feed, which is then transmitted via cloud communication services, allowing for enhanced visual and auditory communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud communication services are used for video conferences, then accessibility and platform compatibility are improved, but processing latency and consistency across platforms worsen
Solution Approach 1:
The system segments video processing functions into two parts: basic video transmission through cloud communication services, and advanced processing functions (face recognition, object recognition, background processing) executed locally by the AV-system. This segmentation allows the system to maintain platform compatibility through cloud services while reducing latency by performing critical processing locally without relying on cloud-based image processing APIs.
2Ease of manufacture
If cloud-based image processing is used, then implementation simplicity is improved, but processing speed and reliability worsen
Solution Approach 1:
The AV-system acts as an intermediary between the cloud communication service and the video processing functions. It receives video feeds from participants via cloud services and performs advanced processing locally using embedded functionality service software. This intermediary approach maintains implementation simplicity by leveraging cloud services for basic transmission while achieving faster processing speeds through local execution of processing algorithms.
3Reliability
If multi-person face and object recognition is implemented, then user experience quality is improved, but system complexity increases
Solution Approach 1:
The AV-system incorporates self-service capabilities through embedded functionality service software that automatically performs multi-person face recognition, object recognition, and background processing without requiring external cloud-based image processing APIs. The system autonomously handles these complex processing tasks locally, improving user experience quality while managing system complexity through integrated software solutions rather than external dependencies.
Data Source
AI summary
Disclosed is a method and a system configured to be arranged at a location. The system being configured for visual and auditory communication between one or more at-location participants and one or more far-end participants. The system comprising an audio/video (AV)-system. The AV-system comprising an audio component for audio transmission and a video component for video transmission. The AV-system is configured for providing a video-feed from the location. The AV-system is configured to connect to a unified communication cloud server for enabling/performing cloud communication service. The system comprising a functionality service software embedded in the AV-system. The functionality service software being configured for controlling a number of functionalities of the video-feed to generate a processed video-feed. The processed video-feed from the location is configured to be provided to the far-end participant(s) via the cloud communication service.