Distributed AI Video Analytics Edge-Cloud Workload Segmentation
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Solution Overview
Problem
Conventional video analytics systems require significant computational resources, manpower, and time to process and analyze video streams from thousands of IoT devices, leading to latency and inefficiencies in real-time event detection and decision-making in smart city applications.
Innovation Solution
A distributed deep learning system that intelligently distributes AI workload across edge devices, EdgeCloud servers, and cloud backends using adaptive data fusion algorithms, enabling real-time video scene parsing and indexing through geo-distributed analytics, with embedded-AI cameras extracting metadata and EdgeCloud servers performing correlation and anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional video analytics systems process video streams from thousands of IoT devices, then comprehensive event detection is achieved, but computational resources, manpower, and time requirements increase significantly
Solution Approach 1:
The system segments the video analytics workload across multiple hierarchical levels: edge devices perform local metadata extraction, EdgeCloud servers handle correlation and anomaly detection, and cloud backends manage storage and indexing. This segmentation enables comprehensive event detection while reducing the computational burden on any single system component, thereby improving overall processing efficiency.
Solution Approach 2:
The patent introduces an intermediary EdgeCloud layer between edge devices and cloud backends. This intermediary performs correlation and anomaly detection, filtering and pre-processing data before it reaches the cloud. This reduces the amount of data transmitted to and processed by cloud servers, significantly improving processing efficiency while maintaining detection accuracy.
2Measurement precision
If manual analysis of video streams is performed, then detailed inspection is possible, but time consumption and resource requirements increase
Solution Approach 1:
The system implements automated self-service analytics where edge devices automatically extract metadata, EdgeCloud servers automatically perform correlation and anomaly detection, and the system automatically indexes data in the cloud. This eliminates the need for manual analysis while maintaining high measurement precision through sophisticated automated algorithms, thereby significantly reducing analysis time.
Solution Approach 2:
The system performs preliminary actions by extracting and indexing metadata at the edge and EdgeCloud levels before final analysis is needed. This pre-processing enables rapid query response and reduces the time required for comprehensive analysis, as the foundation of structured data is already in place.
3Speed
If real-time video processing is implemented, then rapid event detection is achieved, but computational load and latency requirements increase
Solution Approach 1:
The computational workload is segmented across hierarchical levels with different capabilities: edge devices handle lightweight metadata extraction, EdgeCloud servers handle correlation and anomaly detection, and cloud backends handle storage and indexing. This segmentation enables real-time event detection at high speed while distributing the computational power requirement across multiple tiers rather than concentrating it.
Solution Approach 2:
Different levels of the system perform different types of computation appropriate to their location and capabilities. Edge devices perform local metadata extraction with lower computational power requirements, while EdgeCloud and cloud backends handle more computationally intensive tasks. This local quality approach enables real-time processing while optimizing computational resource usage.
4Productivity
If distributed AI workload is implemented, then processing efficiency improves, but system complexity increases
Solution Approach 1:
The patent designs a universal distributed architecture where edge devices, EdgeCloud servers, and cloud backends all participate in the same unified workflow for video analytics. Each component performs a specific function but can work with the same data formats and protocols, simplifying integration. This multi-functionality approach improves processing efficiency while managing system complexity through standardization.
Solution Approach 2:
The system implements feedback mechanisms where the EdgeCloud layer receives metadata from edge devices, performs correlation and anomaly detection, and provides feedback about data quality and processing status. This feedback loop enables efficient workload distribution and data filtering, improving processing efficiency while the automated feedback management reduces the operational complexity of the distributed system.
Data Source
AI summary
System, methods, and algorithms are disclosed to carry out real-time video scene parsing and indexing in conjunction with query-based retrieval of geographically distributed object-attribute relationships. A distributed video analytics query mechanism is disclosed that involves swarms of small deep neural networks at embedded-AI edge devices, which can quickly perform initial feature detection and extraction and also re-identification of features or object in a cooperative manner. Then, the high-volume edge inference may fall back to the query computing model in a cloud, which performs complementary large scale up processing and result generation. The final decision, labelling, and scene investigation may be done by humans after interpreting the query results. This approach can provide the benefit of low communication costs (edge to cloud) compared to continually offloading parallel streams of edge devices, such as video, to the cloud.