Edge Video Querying via Fog Node Feature Matching
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Solution Overview
Problem
The scalability and privacy challenges in video surveillance systems, particularly in Smart Cities, where millions of cameras generate vast amounts of video data, making it impractical for human operators to monitor in real-time and identify objects of interest efficiently, while also raising concerns about privacy invasion.
Innovation Solution
A system that utilizes edge computing and a distributed sensor network to enable real-time video querying and object detection, allowing for instant identification of objects of interest across thousands of frames without relying on cloud processing, thus addressing scalability and privacy concerns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If millions of surveillance cameras are deployed for continuous video collection, then situation awareness and information collection are improved, but privacy invasion concerns increase and human operators cannot efficiently identify objects of interest in real-time
Solution Approach 1:
The patent replaces the mechanical system of human operators manually reviewing video feeds with an automated computer vision system. The system uses deep learning models and object detection algorithms to automatically identify objects of interest in video streams, substituting human visual processing and decision-making with computational algorithms that can process multiple camera feeds simultaneously without fatigue or privacy concerns.
Solution Approach 2:
The patent introduces an intermediary automated processing layer between the camera network and human operators. This intermediary system pre-processes video data, identifies potential objects of interest, and presents filtered results to operators, reducing their workload from monitoring all feeds to reviewing only flagged events that require human judgment.
2Reliability
If human operators monitor hundreds of video screens to identify suspicious activities, then real-time detection capability is maintained, but scalability is severely limited and operator burden increases
Solution Approach 1:
The patent segments the video surveillance system into distributed processing nodes, each handling specific camera feeds or geographic regions. The object detection is divided into independent processing units that can be scaled horizontally by adding more nodes. Each node runs independent instance of object detection algorithms, allowing the system to scale to handle thousands of camera feeds without requiring proportional increase in human operators.
Solution Approach 2:
The patent changes the operational parameters from human-capacity-limited monitoring to computer-capable high-throughput processing. The system processes video frames at speeds far exceeding human reaction times, with configurable detection sensitivity thresholds and adjustable processing priorities for different object types, enabling scalable deployment across diverse surveillance needs.
3Power
If video data is transmitted to cloud centers for processing, then computational power is sufficient for complex analysis, but data transmission time increases and network bandwidth is consumed
Solution Approach 1:
The patent transitions from centralized cloud processing to a distributed edge computing architecture. Object detection and initial analysis are performed at the network edge, closer to the camera sources, using deployed computing devices and local servers. This dimensional shift in processing location reduces data transmission requirements to only essential results and alerts, minimizing latency while maintaining analytical capability.
Data Source
AI summary
A method for querying data obtained from a distributed sensor network, comprising: receiving sensor data representing an aspect of an environment with a sensor of the distributed sensor network; communicating a representation of the sensor data to a fog node through an automated communication network; determining, by the fog node, a correspondence of a query received through the automated communication network to characteristics of the representation of the sensor data; and selectively communicating, in response to the query, at least one of: the sensor data having the determined characteristics corresponding to the query, an identification of the sensor data having the determined characteristics corresponding to the query, and the data representing the sensor data having the determined characteristics corresponding to the query.