Security System Parameter Adjustment via Content Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing network-connected security systems face challenges in optimizing adjustable parameters such as motion detection sensitivity, audio sensitivity, and luminance gain, leading to increased content quality and reduced false positives/negatives in alerts.
Innovation Solution
A technique for analyzing content generated by network-connected security systems to adjust parameters, where content is analyzed at a base station or network-connected computer server, optimizing parameters based on the characteristics of individual or multiple surveillance environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If adjustable parameters (motion detection sensitivity, audio sensitivity, luminance gain) are optimized based on content analysis, then content quality is improved and false positives/negatives are reduced, but device complexity increases due to the need for content analysis systems and parameter adjustment mechanisms
Solution Approach 1:
The security system performs self-optimization by automatically analyzing its own generated content (video, audio, metadata) and adjusting its detection parameters without external intervention. The system uses its own operational data to train models and refine parameters, enabling autonomous improvement of alert accuracy and reduction of false positives/negatives.
Solution Approach 2:
The system implements a feedback loop where content generated by the security system is analyzed to evaluate performance, and this analysis feeds back into parameter adjustments. The analyzed content (video, audio, metadata) is used to train machine learning models that automatically optimize detection sensitivity, audio sensitivity, and luminance gain parameters based on real-world performance data.
2Adaptability or versatility
If content analysis is performed at base station or network-connected computer server to optimize parameters, then parameter optimization capability is improved, but loss of time increases due to the time required for content analysis and parameter adjustment cycles
Solution Approach 1:
The system performs preliminary content analysis and parameter optimization during off-peak times or in advance of critical security monitoring periods. By pre-training models and pre-optimizing parameters using historical data, the system reduces real-time adjustment delays and enables faster response when actual security events occur.
Solution Approach 2:
The system implements periodic content analysis cycles where parameters are automatically adjusted at scheduled intervals rather than continuously. This periodic approach allows the system to optimize parameters using accumulated data while maintaining operational security monitoring, balancing optimization capability with time loss through structured analysis cycles.
Data Source
AI summary
Systems and methods are described for adjusting the parameters in a network-connected security system based on analysis of content generated by electronic devices in the network-connected security system. In an example embodiment, content such as video captured by a video surveillance camera is processed to analyze the performance of the network-connected security system. Based on the processing, updated parameters are selected to configure and improve the performance of the network-connected security system.