Machine Learning Surveillance System Adaptive Granularity
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Solution Overview
Problem
Current surveillance monitoring systems face challenges in efficiently managing resource usage, particularly in detecting situations requiring temporary increases in resource utilization, and in determining compliance with physical standards in real-time scenarios.
Innovation Solution
The implementation of a machine learning system that trains models to identify conditions necessitating fine-grained observation, allowing for reduced resource usage in normal conditions and transitioning to high granularity when specific targets are detected, while also determining compliance with physical standards by evaluating sensor data and granting conditional privileges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full power of cameras, microphones and other detectors is used for continuous surveillance, then detection capability is improved, but resource consumption increases substantially
Solution Approach 1:
The surveillance system dynamically adjusts its operation between two modes: a low-power coarse-granularity mode for normal surveillance, and a high-power fine-granularity mode when hazards are detected. The system transitions from continuous full-power operation to conditional full-power operation, making the detection capability adaptive rather than static.
Solution Approach 2:
The system implements periodic surveillance at coarse granularity to monitor for hazard conditions, and only activates fine-grained observation periodically when the coarse monitoring detects suspicious patterns or potential hazards. This periodic activation of full-power detectors resolves the contradiction by limiting high resource consumption to only when necessary.
2Measurement precision
If fine-grained observation is used continuously, then observation detail is improved, but resource usage increases
Solution Approach 1:
The system applies different observation qualities to different situations: coarse-grained observation for normal conditions and fine-grained observation for hazardous conditions. Rather than uniformly applying fine-grained observation everywhere and always, the system locally adapts the observation quality to match the actual threat level, conserving resources while maintaining detection accuracy when needed.
Solution Approach 2:
The system changes the granularity parameter of observation based on detected conditions. When hazard potential is detected, the system transitions from coarse-grained to fine-grained observation by adjusting parameters such as camera resolution, sampling rate, or sensor activation levels. This parameter change allows the system to achieve detailed observation only when necessary, resolving the energy consumption contradiction.
3Productivity
If automated recognition of situations is implemented, then resource management is improved, but system complexity increases
Solution Approach 1:
The system introduces an automated recognition layer that acts as an intermediary between raw sensor data and resource allocation decisions. This intermediary analyzes coarse-grained data to identify potential hazards and triggers fine-grained observation only when necessary. The added complexity of automated recognition is offset by the elimination of manual monitoring and the efficient resource management it enables.
Solution Approach 2:
The surveillance system performs self-service through automated hazard recognition and autonomous decision-making about when to activate high-power detectors. The system monitors its own operational context, automatically adjusts its resource consumption based on detected conditions, and manages its own surveillance strategy without external intervention. This self-service capability improves resource management efficiency despite the increased complexity of the automated control system.
Data Source
AI summary
A method and system where a first subsystem makes observations and performs surveillance using sensors in a mode that conserves a resource such as power, data transmission band width or processing cycles. This is accomplished by reducing illumination, pixel count, sampling rate or other functions that result in a limited granularity or data collection rate. A machine model is applied to the limited data and, when it evaluates to a suitable result or a prediction of an interesting condition, another subsystem or the same subsystem in a different mode collects data at a finer granularity with a higher data collection size or rate and evaluates that data to determine the nature of the first evaluation. The machine model may be trained in stages on a large scale server and on a small field processor. Data from the sensor may be used for training to improve the second step.