Edge AI Video Surveillance for Contextual Drift Detection
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Solution Overview
Problem
Conventional remote video surveillance systems for large industrial sites face challenges in balancing cost, coverage, image quality, staffing, and network connectivity, leading to inefficiencies and safety risks due to incomplete coverage, poor image quality, inattentional blindness, and limited event documentation.
Innovation Solution
A system utilizing edge devices with AI models for real-time anomaly detection, including a drift detection neural network, segmentation, and temporal context analysis, to create an insight knowledge base that identifies deviations from a contextual baseline, optimizing surveillance with localized data processing and selective storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution cameras with advanced features are deployed to improve image quality and coverage, then monitoring effectiveness is improved, but financial cost increases significantly
Solution Approach 1:
The patent extracts and processes only the most critical visual information (anomalies, events of interest) rather than transmitting and storing complete high-resolution video streams. Edge devices perform local analysis to identify and extract only relevant frames or segments for further processing, significantly reducing data volume and associated costs while maintaining monitoring effectiveness.
Solution Approach 2:
The surveillance system is segmented into distributed edge devices deployed throughout the site, each independently analyzing local video feeds. This segmentation allows parallel processing of multiple camera feeds without requiring centralized high-power infrastructure, reducing overall system cost while maintaining comprehensive coverage capability.
2Area of stationary object
If multiple high-resolution cameras are deployed to cover extensive areas, then coverage is improved, but network infrastructure and storage requirements increase substantially
Solution Approach 1:
Edge devices perform preliminary analysis and filtering of video data locally before transmitting to central systems. By pre-processing and identifying only anomalous or significant events at the edge, the system reduces the amount of data requiring network transmission and centralized storage, simplifying infrastructure requirements while maintaining extensive coverage.
Solution Approach 2:
The system extracts and transmits only the most critical data segments (anomaly detections, key event frames) rather than complete video streams. This extraction approach dramatically reduces network bandwidth requirements and storage needs, enabling extensive area coverage without proportionally increasing infrastructure complexity.
3Reliability
If comprehensive video surveillance is implemented to ensure complete coverage, then detection capability is improved, but staffing requirements increase due to the complexity of monitoring multiple streams
Solution Approach 1:
The edge devices perform self-service anomaly detection and classification without requiring constant human intervention. The system automatically identifies, analyzes, and prioritizes events of interest, reducing the cognitive load on monitoring staff and enabling reliable detection across multiple camera streams without proportionally increasing staffing requirements.
Solution Approach 2:
The system implements feedback loops where detected anomalies are immediately flagged and can trigger automated responses or alert specific monitoring personnel. This feedback mechanism ensures reliable detection while reducing the need for continuous human review of all video streams, as the system self-regulates and prioritizes critical events.
4Reliability
If continuous monitoring of specific behaviors is performed to ensure safety, then monitoring effectiveness is improved, but cognitive effort and attention required by staff increase
Solution Approach 1:
The patent replaces the mechanical human cognitive processing of video streams with automated edge computing systems. AI models and computer vision algorithms perform the analysis of specific behaviors and anomaly detection, substituting human cognitive effort with computational processes that operate continuously without fatigue while maintaining or improving monitoring effectiveness.
Data Source
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AI summary
Disclosed is a computer-implemented method for monitoring of a site. The method includes generating an insight knowledge base based on at least one recurring visual input received from one or more visual capturing devices installed at the site. Further, the method includes detecting a contextual drift at the site based on comparing a real-time feed received from the one or more visual capturing devices and the insight knowledge base using a drift detection neural network model. Further, the method includes selecting the visual frames corresponding to a plurality of timestamps from the real-time feed in response to detecting the contextual drift. The one or more visual frames selected for the timestamps corresponding to, before the contextual drift, during the contextual drift, and post the contextual drift. Further, the method includes transmitting the visual frames for remote monitoring and anomaly detection at the site.