Source-End Video Pre-Processing for WAN Bandwidth Reduction
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Solution Overview
Problem
Current video analytics systems face limitations in scalability and flexibility due to finite processing capabilities within Local Area Networks, leading to high costs and maintenance requirements when deploying 'smart' cameras and encoders at the edge, and the inability to efficiently transmit large amounts of video data across Wide Area Networks.
Innovation Solution
Pre-processing video data using video analytics at the source end to reduce bandwidth requirements, allowing for transmission over Wide Area Networks and subsequent processing in a centralized cloud environment, which supports parallel processing and expansion without the need for extensive local infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video data is transmitted across Wide Area Networks without pre-processing, then complete video data is available for analysis, but network bandwidth requirements exceed available bandwidth and transmission costs increase
Solution Approach 1:
The system extracts only the essential video data elements needed for analytics processing and transmits them to the cloud, rather than transmitting complete video streams. This extraction approach maintains the reliability of analytics functionality while significantly reducing network bandwidth consumption.
Solution Approach 2:
Video pre-processing is performed at the source end before transmission occurs. This preliminary action reduces the data volume that needs to be transmitted across the network, thereby reducing bandwidth requirements and transmission costs while preserving the essential information needed for analytics.
2Productivity
If 'smart' cameras and encoders with built-in video analytics are deployed at the edge, then real-time video analytics capability is improved, but system cost and device complexity increase significantly
Solution Approach 1:
The video analytics system is segmented into two parts: basic pre-processing functionality remains in simple cameras at the edge, while advanced analytics processing is separated and performed in the cloud. This segmentation allows cameras to remain simple and cost-effective while still providing comprehensive video analytics capabilities through the distributed architecture.
Solution Approach 2:
A cloud-based video analytics service acts as an intermediary between simple cameras and end-users. The cloud service receives pre-processed video data from basic cameras and performs advanced analytics, eliminating the need for expensive smart cameras while maintaining high video analytics capability.
3Loss of energy
If video data is pre-processed at the source end, then network bandwidth requirements are reduced, but processing capability at the source end must be sufficient
Solution Approach 1:
The cameras perform only partial video processing - specifically, pre-processing operations that reduce data volume for transmission. Advanced analytics operations are left for cloud processing. This partial action approach reduces network bandwidth requirements while keeping source end device complexity manageable.
Data Source
AI summary
A method for performing video analytics includes receiving at a source end video data including first video data relating to an event of interest. Using video analytics, other than a data compression process, pre-processing of the video data is performed at the source end to reduce the bandwidth requirement for transmitting the video data to below a bandwidth limit of a Wide Area Network (WAN) over which the video data is to be transmitted. The pre-processed video data is transmitted to a central server via the WAN, where other video analytics processing of the pre-processed video data is performed. Based on a result of the other video analytics processing, a signal is generated for performing a predetermined action, in response to an occurrence of the event of interest at the source end.