Video Fire Detection Using Statistical Gaussian Models
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Solution Overview
Problem
Traditional fire detection systems, including dual and triple IR systems and vision-based systems, face challenges in accurately distinguishing fires from non-fire sources, leading to false alarms and inefficiencies in environments with complex conditions.
Innovation Solution
A video-based fire detection system employing a block-based statistical Gaussian measurement scheme with a training phase and detection phase, utilizing a Gaussian model for RGB pixel statistics and Jeffreys divergence calculations to identify fire regions, and incorporating temporal difference analysis to differentiate between fires and non-fire events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a three-dimensional RGB histogram is generated using training video frames to represent fire color, then fire detection accuracy is improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the color space into discrete color intervals along the red-green-blue axes, dividing the continuous RGB histogram into manageable bins. This segmentation transforms the complex continuous color distribution analysis into discrete interval comparisons, reducing computational complexity while maintaining fire detection accuracy.
Solution Approach 2:
The patent changes the parameter representation from continuous RGB values to discrete color interval memberships. By transforming the color space parameters into interval-based representations with membership functions, the system reduces the dimensional complexity of histogram generation and enables more efficient fire detection calculations.
2Reliability
If color, contrast, and texture analysis is performed followed by temporal difference analysis, then fire detection capability is improved, but system response time and processing duration increase
Solution Approach 1:
The patent performs preliminary color interval classification and contrast analysis before temporal difference analysis. By pre-establishing color membership functions and contrast thresholds in advance, the system prepares detection criteria beforehand, enabling faster real-time fire detection without compromising reliability.
Solution Approach 2:
The patent applies partial action by focusing analysis on specific color intervals most characteristic of fire (red and yellow regions) rather than analyzing the entire RGB spectrum equally. This selective analysis reduces processing time while maintaining detection reliability by concentrating computational resources on the most discriminative features.
Data Source
AI summary
A fire detection system has a video sensor, a processor, and a database. The database has a statistical model representing the characteristics of a fire. The video sensor captures images, and sends the images to the processor. The processor computes statistics on the characteristics of the captured image, and compares it to the statistical model to determine if a fire is present in the captured image.