fire detection for early fire alarm based on optical flow video processing

Fire Detection for Early Fire Alarm Based onOptical Flow Video Processing

Suchet Rinsurongkawong1, Mongkol Ekpanyapong, and Matthew N. Dailey

Mechatronics, suchet.rinsurongkawong@ait.ac.th

Microelectronics and Embedded systems, mongkol@ait.ac.th

Computer Science and Information Management, mdailey@ait.ac.th

Asian Institute of Technology, Pathumthani, Thailand

Outline

• Introduction• Methods• Experience result• Future work

Introduction

• Fire has always threatened properties and peoples’ lives.

• Most conventional fire detection technologies are based on particle sampling, temperature sampling, and smoke analysis,but fire detection systems using these technologies have limited effectiveness due to high false alarm rates.

• Because of the rapid developments in digital camera technology and computer vision system, there are many fire detection technologies which are introduced based on image processing.

Moving region detection

• Background subtraction:

• Be assumed to be a moving pixel if:

Chromatic features(1/3)

• The color of fire always appears in red-yellow range.

Chromatic features(2/3)

• To solve from a fire-like color.

Chromatic features(3/3)

• Besides, when the fire is in dark background environment without other background illumination, the fire will be the main light source. From this reason, the fire may display in a whole white color in an image. Thus, the intensity should be over threshold intensity IT .

Growth rate analysis

• The growth rate rule can be deduced as:

• Where Gi denotes quantities of the current frame to the n th frame.

• If the result is more than a reference Gr from the first detected frame, the moving object will be considered as a real flame.

Turbulent fire plumes

Turbulent fire plumes

• The frequency shows the cycle times of eddies effect per 1 second.

• Where f denotes a vortex shedding frequency in Hz for a fire of diameter D in meters.

Lucas-kanade optical flow pyramid

• The algorithm of LK is based on 3 assumptions.

1. “Brightness constancy”

2. “Temporal persistence”

3. “Spatial coherence”

Flow rate analysis(1/3)

• From the previous step, the LK optical flow can extract the motion velocity vector from each feature point.

• Where p and q denote the starting and the ending point of each feature point respectively. n refers to the number of feature points.

Flow rate analysis(2/3)

• The average flow rate of the first time of optical flow analysis is calculated as follow:

• Where Fa denotes the average flow rate of the first detected time for optical flow analysis. This first average flow rate will be used as a reference value for next n time calculation.

Flow rate analysis(3/3)

• variation of flow rate:

• Where Fv is the average flow rate from n time calculation,we will called it “variation of flow rate”. Due to the turbulent of flame, the variation flow rate of fire will give a significant value more than other moving objects.

Expermental result

• Find the flow rate threshold value

Method1 & method2

Result from method1

Conclusion and future

• In dynamic analysis, the combination of growth rate and Lucas-Kanade optical flow can extract the motion feature of fire, so this method can easily distinguish the disturbances which having the same color distribution as fire.

• In the future, the neural network will be applied to train the raising parameters composed of fire-pixels extracted at timeinterval fur increasing the reliability of fire-alarming. The use of neural networks, the statistical values must have highly enough in the training process.

Thanks for your attention!