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!