5G Multi-Camera Stream Synchronization With Edge Processing
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Solution Overview
Problem
Existing systems face challenges in simultaneously collecting and processing large numbers of images and videos from multiple cameras in a distributed manner, experiencing high image and video traffic, substantial network requirements, and long delays, particularly in the context of power equipment maintenance.
Innovation Solution
A method and system utilizing distributed edge computing and 5G communication to synchronize image and video capture across multiple cameras, perform local processing, and transmit sub-packages with timestamp protocols for synchronized recovery at a backend server, reducing bandwidth pressure and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple cameras simultaneously capture images and video streams and transmit them to a backend server, then real-time monitoring and inspection can be achieved, but network bandwidth requirements increase substantially and transmission delays occur
Solution Approach 1:
The patent divides the image and video data processing into two segments: edge processing at the camera end and central processing at the backend server. The camera performs local processing including feature extraction, edge detection, and video stream packaging, thereby segmenting the data transmission volume and reducing the burden on the network and backend server.
Solution Approach 2:
The patent extracts essential information from the original image and video streams at the camera end through processing operations such as feature point calculation and edge non-linear trimming. Only the processed and essential data is transmitted to the backend server, extracting the necessary information while reducing unnecessary data transmission.
2Device complexity
If images and video streams from multiple cameras are transmitted directly to the backend server, then centralized processing can be simplified, but transmission latency increases and network requirements become substantial
Solution Approach 1:
The patent performs preliminary processing actions at the camera end before data transmission to the backend server. This includes preliminary feature extraction, edge detection, and video stream packaging operations, which are completed locally at the camera before transmission, thereby reducing transmission time and latency.
Solution Approach 2:
The patent introduces an intermediary processing layer at the camera end that performs local processing before transmission. This intermediary layer acts as a buffer between the camera and backend server, processing data locally and only transmitting necessary processed information, thereby reducing transmission delay and network requirements.
3Productivity
If distributed edge computing is implemented at multiple cameras, then processing efficiency improves and bandwidth pressure is reduced, but system complexity and synchronization requirements increase
Solution Approach 1:
The patent changes the processing parameters and operations at the camera end through programming configurations. By adjusting processing parameters such as feature extraction thresholds, edge detection parameters, and video packaging settings, the system achieves efficient distributed processing while managing complexity through parameter optimization rather than structural complexity.
Data Source
AI summary
A method for synchronized and distributed processing multi-channel image and video stream based on 5G environment is provided, including: transmitting multi-channel the same pulse signals to a data collecting circuit by a pulse transmitter, the data collecting circuit transmits the pulse signal to an image capturing circuit, and the image capturing circuit triggers a camera capturing system to start to simultaneously capture images and video stream. Distributing multi-channel traffic of the cameras via distributed edge computing and distribution processing, so as to locally process the captured images and video stream directly. Such that, the bandwidth pressure is significantly reduced and the risk of terminal sensitive data information privacy leakage is reduced, too. Performing same data-time classification on the captured images and video stream by using timestamp protocol, and finally performing, on the classified images and video stream, a synchronized and distributed sub-packaging and transmission to a backend processing server.